Towards a Science of Causal Interpretability in Deep Learning for Software Engineering

David N. Palacio

REVIEW 2 major objections 5 minor 300 references

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This dissertation claims that neural code models can be understood causally by treating code properties as interventions in structural causal models, and that docode makes this practical.

desk verdict The dissertation's central confounding-bias illustration compares a bounded distributional divergence to a mean-difference ATE, and that mismatch, not just unmeasured confounders, is the real obstacle to accepting its flagship claim. read the letter →

arxiv 2505.15023 v1 pith:N45TVOQY submitted 2025-05-21 cs.SE cs.AI

David N. Palacio This is my paper

classification cs.SEcs.AI

keywords causalinterpretabilityneuralcodemodelsstructuraldeeplearningforsoftwareengineeringgenerationconfoundingbiasaveragetreatmenteffectposthocexplanation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This dissertation tries to establish that the predictions of neural code models (NCMs) can be explained causally, not just correlationally, by treating software-engineering properties as treatments in structural causal models. It introduces docode, a four-step post hoc method that models a causal problem, identifies an estimand, estimates effects such as the average treatment effect, and refutes the estimate. The payoff would be a principled way to tell when an observed correlation between a code property and model performance reflects a real cause and when it is an artifact of confounding. In the central case study, the author claims that the presence or absence of buggy code did not causally influence prediction performance despite a high measured correlation, a signature of confounding bias. A sympathetic reader would summarize the contribution as moving interpretability for code models from 'what correlates' to 'what happens if we intervene'.

What carries the argument

The load-bearing mechanism is the Structural Causal Model (SCM), a directed acyclic graph with structural equations that encodes assumptions about which software properties influence which model outcomes. docode's machinery is the four-step pipeline built on it: model the causal problem as an SCM, identify a causal estimand such as p(Y|do(T)) via do-calculus and back-door adjustment, estimate the effect with metrics like the Average Treatment Effect (ATE), and refute the estimate through placebo and robustness tests. Interventions are software-engineering-based perturbations, such as buggy versus fixed code, commented versus uncommented code, clone types, and AST node masking, applied to parallel code corpora.

What would settle it

A concrete check: take a model and dataset from the case study, randomly assign code snippets to buggy versus fixed treatments, and measure the difference in cross-entropy under this randomized assignment; if the randomized effect is non-negligible while docode's back-door ATE is near zero, then the SCM's confounder set was incomplete and the causal claim fails.

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Extended reading notes

Core claim

The central claim is the Causal Interpretability Hypothesis: docode is a causal interpretability method that aims to make NCMs and their decision-making understandable by interpreting model prediction performance through Pearl's Ladder of Causation. Concretely, the dissertation reports that under docode's interventions, bugginess of code did not appear to causally influence prediction performance even when association was high, while Type II clones in training data did causally affect cross-entropy. It also reports that masking random tokens hurt BERT-like models more than masking grammar-based categories, that most studied models learned code-block tokens with less confounding bias, and that prompt semantics causally influenced ChatGPT performance in the Galeras benchmark. The author presents these as evidence that causal estimands can reveal confounding bias that associational interpretability misses.

Load-bearing premise

The load-bearing premise is that each SCM includes all relevant confounders, so that back-door adjustment recovers the true causal effect; the dissertation itself flags in Sec 3.6.3 that unidentified confounders could bias the relationship.

Editorial extensions

If this is right

If the case study is right, benchmark comparisons of NCMs that rely on correlations or accuracy alone can rank models on spurious signal; causal estimates should be reported alongside.
If docode estimates are valid, practitioners can distinguish code properties worth fixing from harmless correlates, guiding data curation and model debugging.
The finding that grammar-based masking matters less than random masking for BERT-like models would imply these models may not encode full syntactic structure.
The result that buggy code shows high association but near-zero causal effect would imply that some commonly reported 'bugginess hurts models' findings may be confounded by features like token count.
The success of refutation testing in the pipeline would imply that causal interpretability can be made falsifiable, unlike purely descriptive explanation methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: if the same back-door adjustment were applied to other code properties, such as identifier naming style, comment density, or code churn, many reported correlation-based findings in DL4SE might shrink or reverse.
Editorial inference: docode's framework suggests a testable standardization where model cards report ATE, association, and the confounder set, so causal claims accumulate across studies.
Editorial inference: the four-confounder set used in the case study is only a starting point; adding token frequency or training-data leakage as confounders could change the estimates, which is a direct empirical extension using the same pipeline.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

What you should know about this dissertation: the flagship demonstration of confounding bias in Section 2.4.1 compares a Jensen-Shannon divergence (bounded 0–1) to an ATE in cross-entropy units and reads the gap as evidence of spurious correlation. That comparison doesn't support the conclusion. A nonzero JSD can coexist with a zero mean difference—the treatment could change the shape or variance of the outcome distribution without moving the mean. So the headline finding that buggy code has no causal influence on NCM performance is not established by the numbers as presented.

There's real substance here, though. The docode pipeline (SCM, estimand, estimation, refutation) is a standard causal inference workflow from doWhy, but the translation to neural code models with SE-based interventions—buggy/fixed, commented/uncommented, Type II/III clones, AST node masking—is new and practically motivated. The case study is extensive: ten NCMs, seven scenarios, refutation tests, and released artifacts (docode, ASTrust, CodeQ, Galeras). The ASTrust chapter's attempt to causally link syntax-category confidence to cross-entropy is a useful idea, and the finding that certain categories affect learning error after controlling for confounders is genuinely interesting.

The soft spots, in order of severity. First, the metric mismatch above. The paper should re-express the association as a mean difference or standardized effect size so it's comparable to the ATE. Without that, the confounding-bias story is just a comparison of incomparable numbers. Second, the hand-specified SCMs use only four confounders, with no sensitivity analysis, and the author explicitly acknowledges unidentified confounders (Sec 3.6.3). That makes the ATE estimates conditional on the chosen adjustment set—reasonable, but weaker than the text sometimes implies. Third, the human study is small (24 valid responses) and purposive, so usability claims are preliminary. The speculative autopoietic chapter (Ch 10) is disconnected from the rest and can be safely ignored.

The reader's conditional verdict is fair in spirit, but the condition should be the metric re-expression, not just unmeasured confounding. The causal estimates themselves may be valid; it's the diagnosis of confounding from JSD-vs-ATE that needs rework. This is a dissertation, not a focused paper, so it's uneven, but the empirical core and artifacts are worth engaging. I'd send it to peer review with a request for major revision on the association-vs-effect comparison. Researchers in interpretability of code models and causal inference in SE will get value from the framework and testbeds.

Referee Report

2 major / 5 minor

Summary. This dissertation introduces docode, a post hoc causal interpretability method for Neural Code Models (NCMs), and evaluates it across multiple studies. The method is presented as a four-step pipeline: structural causal model construction, causal estimand identification, effect estimation via Average Treatment Effect (ATE), and refutation testing. The empirical core is a deep-code-generation case study spanning several NCM architectures and data interventions (buggy/fixed code, comments, clone types, AST node masking), supplemented by chapters on ASTrust, COMET, TraceXplainer, CodeQ, and the Galeras benchmark. The headline claim is that observed associations and causal effects diverge—for example, buggy code is correlated with NCM performance but allegedly does not causally influence it—and that docode can identify such confounding bias.

Significance. If the causal estimates were externally validated, docode would be a genuinely useful addition to DL4SE: it is explicitly grounded in Pearl's do-calculus, the case study covers a diverse set of architectures and datasets, several replication packages are released, and the proposed four-step framework gives practitioners a concrete starting point. The dissertation also states important limitations, including the possibility of unidentified confounders in Sec. 3.6.3. However, the central demonstration of confounding bias rests on a scale mismatch between a bounded distributional divergence (Jensen-Shannon distance) and a mean difference in cross-entropy units, and the hand-specified SCMs are not validated against any external causal ground truth. These issues materially weaken the empirical conclusions as currently stated, although they appear addressable with re-analysis and additional sensitivity testing.

major comments (2)

[§2.4.1, Table 8.4, Definitions 7.4 and 8.1] The flagship example of confounding bias compares a Jensen-Shannon distance (bounded on [0,1]) with an ATE in cross-entropy units. These quantities are not on a common scale: a non-zero JSD can coexist with a zero mean difference when the treatment changes the variance, shape, or a small subset of the outcome distribution. Consequently, the reported contrast between JSD ≈ 0.67 and ATE ≈ −2E−4 does not by itself establish spurious correlation or confounding bias. The same issue affects Table 8.5, where Pearson correlations are compared with ATEs. To support the claim that 'buggy code did not appear to causally influence prediction performance', the authors should re-express association and causal effect on the same scale—for example, comparing the associational mean difference E[Y|T=1] − E[Y|T=0] with the back-door adjusted ATE, or comparing interventional and observational distributions with the same distributional metric.
[§3.4.4, §3.6.3, §7.4] The causal conclusions depend on the correctness of hand-specified SCMs whose adjustment sets are not validated against any external benchmark. In the ASTrust validity study, only four confounders are used (cyclomatic complexity, AST levels, AST nodes, sequence size), and the text itself acknowledges in §3.6.3 that unidentified confounders may bias the causal relationship. The placebo refutations described in §7.4 test internal consistency of an assumed SCM but do not test whether the adjustment set is sufficient. The authors should add sensitivity analyses for unmeasured confounding (e.g., E-values or bounds under an omitted confounder), report uncertainty intervals for the ATEs in Tables 8.2–8.6, and, where possible, compare the back-door estimates against a randomized or quasi-experimental intervention.

minor comments (5)

[§3.4.4, Table 3.4] The text states that 14 sub-categories were analyzed, but Table 3.4 lists 13 rows; the count and the table should be reconciled.
[Table of Contents, Chapter 5] The heading '5.7 A Case Study in Industry' appears twice; the duplicated heading should be corrected.
[Table 3.3, Fig. 3.9] The metric name is inconsistently spelled as 'BLUE-4' and 'CodeBLUE'; it should be BLEU-4 and CodeBLEU.
[Fig. 2.4 caption] The caption uses p(Y|Z) ≈ 0.87 for what is described as a correlation; this notation is confusing and should be replaced with a Pearson correlation coefficient or equivalent.
[Chapter 10] The autopoietic Ψ-Arch chapter is a speculative position with no empirical evaluation; the introduction should clearly frame it as a vision statement rather than a contribution on par with the evaluated tools.

Circularity Check

2 steps flagged · score 4.0 of 10

One validation claim (ASTrust RQ3) reduces partly by construction because treatment and outcome are both derived from the same token-level prediction probabilities, making the negative causal effect an algebraic consequence of the definitions; the core docode methodology itself is externally grounded in Pearl's framework and doWhy, while the flagship confounding-bias contrast (JSD vs ATE) is a…

self definitional [Sec. 3.4.4 (Fig. 3.6 SCM for RQ3) and Sec. 3.5.3 (RQ3 conclusion)]
"We consider that the learning error (i.e., cross-entropy loss) of an LLM is causally impacted by the predicted probabilities of syntax elements. ... Negative effects indicate that the better a syntax category is predicted, the lower the learning error associated."

The outcome Y (cross-entropy loss) is computed as the negative log of each token's predicted probability summed over a snippet, while the treatment T (ASTrust performance for a syntax category) is the median of those same token-level prediction probabilities restricted to tokens in that category. Raising T mechanically lowers the -log(p) contributions of the category's tokens, hence lowers Y. The claimed causal relation T -> Y with a negative sign is therefore baked into the shared definitions: 'the better a syntax category is predicted, the lower the learning error' is an identity of how both quantities are constructed from the same NTP values, not an independent empirical discovery. Only the ATE magnitude is data-driven; its sign is forced.
self citation load bearing [Sec. 3.4.4 (ASTrust validation) and Sec. 8.4 (docode case-study syntax clustering)]
"We conducted a causal inference analysis using the docode technique [PCR+23] to estimate SCs influence."

ASTrust's causal validity (RQ3) is established by applying docode, which is this dissertation's own prior work [PCR+23], and docode's case study (Sec. 8.4, 'Syntax Clustering of Code Predictions') relies on the syntax categories developed in Ch. 3. The two self-citations form a mutual-support loop: ASTrust validates docode's machinery and docode's machinery validates ASTrust, with no external causal ground truth for either. The loop is only partial, not total, because the estimation engine is externally anchored (Pearl's do-calculus and the doWhy implementation), the ATE magnitudes are data-driven, and the artifacts are publicly available and externally falsifiable in principle; nonetheless, the load-bearing validity argument for syntax-grounded explanations is internally generated.

full rationale

The core contribution, docode, is a methodological application of Pearl's Structural Causal Model / do-calculus framework, estimated through the external doWhy library on public NCMs and datasets; that machinery is independent of the paper's own conclusions, so the pipeline itself is not circular by construction. The empirical case-study findings (near-zero ATE for buggy code, positive ATE for Type II clones, token-masking effects) are data-driven estimates, not fitted parameters renamed as predictions. However, two issues warrant the moderate score. First, the RQ3 validation of ASTrust is partially circular: the treatment (per-category prediction confidence) and outcome (cross-entropy loss) are both defined from the same token-level predicted probabilities, so the negative causal effect is largely an algebraic consequence of the definitions; the paper even states the conclusion tautologically ('the better a syntax category is predicted, the lower the learning error'). Second, the dissertation's flagship demonstration of confounding bias (Sec. 2.4.1, Tables 8.2/8.4) contrasts a Jensen-Shannon Distance bounded by [0,1] with an ATE in cross-entropy units; a JSD near 0.67 and an ATE near -2E-4 are incommensurable, so the inference of 'spurious correlation' is a correctness/validity flaw rather than circularity, and per the review rules it is noted here rather than scored as a circular step. The self-citation loop between ASTrust and docode is present but only partially load-bearing given the external Pearl/doWhy anchor and public, externally falsifiable artifacts. Overall, the central claim retains independent empirical content, never reducing to a pure self-citation or definitional identity, hence a score of 4 rather than 6 or higher.

Assumptions & free parameters 3 free parameters · 3 assumptions · 1 invented entities

The central claims of docode rest on hand-built SCMs and the assumption that measured confounders suffice to identify causal effects. The syntax categories and thresholds used to interpret results are also hand-chosen. No new physical entities are proposed; the speculative autopoietic architecture in Chapter 10 is included for completeness.

free parameters (3)

Syntax Categories set = 10 hand-defined categories (Decisions, Data Structures, Exceptions, Iterations, Functional Programming, Operators…
Chosen by hand to interpret predictions; the empirical results in Chapter 3 and the docode case study depend on this grouping.
ASTrust confidence threshold = 0.6
Tunable threshold used to label syntax categories as confidently predicted; set in Sec 3.5.2 and affects which categories are considered learned.
Confounder set for causal analyses = Number of Subwords (ProgramRepair); Cyclomatic Complexity, AST Levels, #AST Nodes, Sequence Size (ASTrust causal study)
Chosen by the author to identify causal effects; the estimated ATEs depend on these selected confounders held in the SCM.

assumptions (3)

domain assumption The back-door criterion is satisfied by the chosen confounders, so the ATE is identifiable from observational data.
Invoked implicitly in docode's estimation step (Sec 7.3) and explicitly in the ASTrust causal study (Sec 3.4.4); the threat of unidentified confounders is acknowledged in Sec 3.6.3.
domain assumption Token probabilities are well-calibrated proxies for the likelihood that a token prediction is correct.
Stated in Sec 3.3.3 and justified by a citation to SGP+24; essential for ASTrust confidence to represent performance.
domain assumption Parallel code corpora (buggy/fixed, commented/uncommented, clone pairs, masked AST nodes) realize the SE-based interventions described.
The case study treats differences between corpora as interventions, but no manipulation check verifies that the hypothesized treatment is the only change (Sec 8.2-8.4).

invented entities (1)

Psi-Arch autopoietic architecture
purpose: Position paper speculating on self-constructing software life-cycles via counterfactual agents
Proposed in Chapter 10 with no implementation or empirical test; it is a conceptual speculation rather than a functioning entity.

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Cite this review

Pith. "Pith review of Towards a Science of Causal Interpretability in Deep Learning for Software Engineering." pith.science (2026). https://pith.science/paper/N45TVOQY

@misc{pith2026250515023,
  author       = {Pith},
  title        = {Pith review of: Towards a Science of Causal Interpretability in Deep Learning for Software Engineering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N45TVOQY}},
  note         = {Machine review of arXiv:2505.15023}
}

read the original abstract

This dissertation addresses achieving causal interpretability in Deep Learning for Software Engineering (DL4SE). While Neural Code Models (NCMs) show strong performance in automating software tasks, their lack of transparency in causal relationships between inputs and outputs limits full understanding of their capabilities. To build trust in NCMs, researchers and practitioners must explain code predictions. Associational interpretability, which identifies correlations, is often insufficient for tasks requiring intervention and change analysis. To address this, the dissertation introduces DoCode, a novel post hoc interpretability method for NCMs. DoCode uses causal inference to provide programming language-oriented explanations of model predictions. It follows a four-step pipeline: modeling causal problems using Structural Causal Models (SCMs), identifying the causal estimand, estimating effects with metrics like Average Treatment Effect (ATE), and refuting effect estimates. Its framework is extensible, with an example that reduces spurious correlations by grounding explanations in programming language properties. A case study on deep code generation across interpretability scenarios and various deep learning architectures demonstrates DoCode's benefits. Results show NCMs' sensitivity to code syntax changes and their ability to learn certain programming concepts while minimizing confounding bias. The dissertation also examines associational interpretability as a foundation, analyzing software information's causal nature using tools like COMET and TraceXplainer for traceability. It highlights the need to identify code confounders and offers practical guidelines for applying causal interpretability to NCMs, contributing to more trustworthy AI in software engineering.

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Reference graph

Works this paper leans on

300 extracted references · 57 canonical work pages

[1]

Asuncion, Arthur U

Hazeline U. Asuncion, Arthur U. Asuncion, and Richard N. Taylor. Software traceability with topic modeling. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1 , ICSE '10, pages 95--104, 2010

2010
[2]

Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng

Mart\' n Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. Tensorflow: a system for lar...

2016
[3]

Learning to represent programs with graphs

Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. Learning to represent programs with graphs. In International Conference on Learning Representations , 2018

2018
[4]

Information retrieval models for recovering traceability links between code and documentation

Antoniol, Canfora, Casazza, and De Lucia. Information retrieval models for recovering traceability links between code and documentation. In Proceedings of the International Conference on Software Maintenance , ICSE'00, pages 40--49, Oct 2000

2000
[5]

Unified pre-training for program understanding and generation

Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. Unified pre-training for program understanding and generation
[6]

Albrecht, Filippos Christianos, and Lukas Sch\"afer

Stefano V. Albrecht, Filippos Christianos, and Lukas Sch\"afer. Multi-Agent Reinforcement Learning: Foundations and Modern Approaches . MIT Press, 2024

2024
[7]

A literature review of automatic traceability links recovery for software change impact analysis

Thazin Win Win Aung, Huan Huo, and Yulei Sui. A literature review of automatic traceability links recovery for software change impact analysis. 2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC) , pages 14--24, 2020

2020
[8]

The adverse effects of code duplication in machine learning models of code

Miltiadis Allamanis. The adverse effects of code duplication in machine learning models of code. In Onward! OOPLSA 2019 , pages 143--153, 2019

2019

Show all 300 references

[9]

Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin

Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning from Data: A Short Course . AMLBook, United States, 2012

2012
[10]

The claude 3 model family: Opus, sonnet, haiku, 2024

Anthropic. The claude 3 model family: Opus, sonnet, haiku, 2024. Preprint

2024
[11]

Program synthesis with large language models, 2021

Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. Program synthesis with large language models, 2021

2021
[12]

Bishop and Hugh Bishop

Christopher M. Bishop and Hugh Bishop. Deep Learning: Foundations and Concepts . Springer International Publishing, 2024

2024
[13]

Gpt-neox-20b: An open-source autoregressive language model, 2022

Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, and Samuel Weinbach. Gpt-neox-20b: An ope...

2022
[14]

Maximum a posteriori estimators as a limit of Bayes estimators , 2018

Robert Bassett and Julio Deride. Maximum a posteriori estimators as a limit of Bayes estimators , 2018

2018
[15]

A neural probabilistic language model

Yoshua Bengio, Réjean Ducharme, and Pascal Vincent. A neural probabilistic language model . Advances in Neural Information Processing Systems , 3:1137--1155, 2003

2003
[16]

Artificial Life

M A Bedau. Artificial Life . Philosophy of Biology , pages 585--603, 2007

2007
[17]

Jasmijn Bastings and Katja Filippova. The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? In Afra Alishahi, Yonatan Belinkov, Grzegorz Chrupa a, Dieuwke Hupkes, Yuval Pinter, and Hassan Sajjad, editors, Proceedings of the ...

2020
[18]

Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency , FAccT '21, page 610–623, New York, NY,...

2021
[19]

Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics) . Springer-Verlag New York, Inc., 2006

2006
[20]

Natural Language Processing with Python

Steven Bird, Ewan Klein, and Edward Loper. Natural Language Processing with Python . O'Reilly Media Inc., 2009

2009
[21]

NLTK : The natural language toolkit

Steven Bird and Edward Loper. NLTK : The natural language toolkit. In Proceedings of the ACL Interactive Poster and Demonstration Sessions , pages 214--217, Barcelona, Spain, July 2004. Association for Computational Linguistics

2004
[22]

API design for machine learning software: experiences from the scikit-learn project

Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Ga \" e l Varoquaux. API design for machine lear...

2013
[23]

A model-driven architecture approach to accelerate software code generation

Mayuri Bhadra, Daniela Sanchez Lopera, Robert Kunzelmann, and Wolfgang Ecker. A model-driven architecture approach to accelerate software code generation. In 2024 7th International Conference on Software and System Engineering (ICoSSE) , pages 23--30, 2024

2024
[24]

On identifiability in transformers, 2020

Gino Brunner, Yang Liu, Damián Pascual, Oliver Richter, Massimiliano Ciaramita, and Roger Wattenhofer. On identifiability in transformers, 2020

2020
[25]

Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffr...

2020
[26]

Biggerstaff, Bharat G

Ted J. Biggerstaff, Bharat G. Mitbander, and Dallas E. Webster. Program understanding and the concept assignment problem. Commun. ACM , 37(5):72--82, May 1994

1994
[27]

tree-sitter/tree-sitter: v0.25.3, March 2025

Max Brunsfeld, Amaan Qureshi, Andrew Hlynskyi, Patrick Thomson, ObserverOfTime, Will Lillis, Josh Vera, dundargoc, Phil Turnbull, Timothy Clem, Douglas Creager, Andrew Helwer, Rob Rix, Daumantas Kavolis, Hendrik van Antwerpen, Michael Davis, Christian Clason, Ika, Amin Ya, Ril...

2025
[28]

Sampling in Software Engineering Research : A Critical Review and Guidelines , October 2021

Sebastian Baltes and Paul Ralph. Sampling in Software Engineering Research : A Critical Review and Guidelines , October 2021. arXiv:2002.07764 [cs]

2021 arXiv
[29]

J. Brooke. SUS : A quick and dirty usability scale. In P. W. Jordan, B. Weerdmeester, A. Thomas, and I. L. Mclelland, editors, Usability Evaluation in Industry . Taylor and Francis , London, 1996

1996
[30]

Ullman, Fernando Martinez-Plumed, Joshua B

Ryan Burnell, Wout Schellaert, John Burden, Tomer D. Ullman, Fernando Martinez-Plumed, Joshua B. Tenenbaum, Danaja Rutar, Lucy G. Cheke, Jascha Sohl-Dickstein, Melanie Mitchell, Douwe Kiela, Murray Shanahan, Ellen M. Voorhees, Anthony G. Cohn, Joel Z. Leibo, and Jose Hernandez...

2023
[31]

Causality: The Place of the Causal Principle in Modern Science

Mario Bunge. Causality: The Place of the Causal Principle in Modern Science . Harvard University Press, 1959

1959
[32]

Emergence and Convergence: Qualitative Novelty and the Unity of Knowledge

Mario Bunge. Emergence and Convergence: Qualitative Novelty and the Unity of Knowledge . University of Toronto Press, 2003

2003
[33]

Philosophy of Science: Volume 1 and 2

Mario Bunge. Philosophy of Science: Volume 1 and 2 . Routledge, 2011

2011
[34]

The care and feeding of wild-caught mutants

David Bingham Brown, Michael Vaughn, Ben Liblit, and Thomas Reps. The care and feeding of wild-caught mutants. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering , ESEC/FSE 2017, page 511–522, New York, NY, USA, 2017. Association for Computing...

2017
[35]

An empirical exploration of trust dynamics in llm supply chains, 2024

Agathe Balayn, Mireia Yurrita, Fanny Rancourt, Fabio Casati, and Ujwal Gadiraju. An empirical exploration of trust dynamics in llm supply chains, 2024

2024
[36]

Nature’s Capacities and Their Measurement

Nancy Cartwright. Nature’s Capacities and Their Measurement . Oxford University Press, 1989

1989
[37]

The Dappled World: A Study of the Boundaries of Science

Nancy Cartwright. The Dappled World: A Study of the Boundaries of Science . Cambridge University Press, 1999

1999
[38]

Hunting Causes and Using Them: Approaches in Philosophy and Economics

Nancy Cartwright. Hunting Causes and Using Them: Approaches in Philosophy and Economics . Cambridge University Press, 2007

2007
[39]

An empirical study on the usage of bert models for code completion

Matteo Ciniselli, Nathan Cooper, Luca Pascarella, Denys Poshyvanyk, Massimiliano Di Penta, and Gabriele Bavota. An empirical study on the usage of bert models for code completion. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) , pages 108-...

2021
[40]

An empirical study on the usage of BERT models for code completion

Matteo Ciniselli, Nathan Cooper, Luca Pascarella, Denys Poshyvanyk, Massimiliano Di Penta, and Gabriele Bavota. An empirical study on the usage of BERT models for code completion. CoRR , abs/2103.07115, 2021

2021 arXiv
[41]

An empirical study on the usage of transformer models for code completion

Matteo Ciniselli, Nathan Cooper, Luca Pascarella, Antonio Mastropaolo, Emad Aghajani, Denys Poshyvanyk, Massimiliano Di Penta, and Gabriele Bavota. An empirical study on the usage of transformer models for code completion. IEEE Transactions on Software Engineering , 48(12):481...

2022
[42]

C. S. Corley, K. Damevski, and N. A. Kraft. Exploring the use of deep learning for feature location. In 2015 IEEE International Conference on Software Maintenance and Evolution ( ICSME ) , ICSME'15, pages 556--560, September 2015. ISSN:

2015
[43]

Counterfactual explanations for models of code

Jürgen Cito, Isil Dillig, Vijayaraghavan Murali, and Satish Chandra. Counterfactual explanations for models of code. In Proceedings of the 44th International Conference on Software Engineering : Software Engineering in Practice , pages 125--134, Pittsburgh Pennsylvania, May 2022. ACM

2022
[44]

Feldman, Arjun Guha, Michael Greenberg, and Abhinav Jangda

Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen, Luna Phipps-Costin, Donald Pinckney, Ming-Ho Yee, Yangtian Zi, Carolyn Jane Anderson, Molly Q. Feldman, Arjun Guha, Michael Greenberg, and Abhinav Jangda. MultiPL - E : A Scalable and Extensible Approach to Benchmark...

2022 arXiv
[45]

Constructing Grounded Theory: A Practical Guide through Qualitative Analysis

Kathy Charmaz. Constructing Grounded Theory: A Practical Guide through Qualitative Analysis . SAGE Publications Inc., 2006

2006
[46]

Chang, and Mark Christensen

Jane Cleland-Huang, Carl K. Chang, and Mark Christensen. Event-based traceability for managing evolutionary change. IEEE Trans. Softw. Eng. , 29(9), September 2003

2003
[47]

A machine learning approach for tracing regulatory codes to product specific requirements

Jane Cleland-Huang, Adam Czauderna, Marek Gibiec, and John Emenecker. A machine learning approach for tracing regulatory codes to product specific requirements. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering , ICSE'10, pages 155--164. ACM, 2010

2010
[48]

Connor, A

A. Connor, A. Harris, N. Cooper, and D. Poshyvanyk. Can we automatically fix bugs by learning edit operations? In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) , pages 782--792, Los Alamitos, CA, USA, mar 2022. IEEE Computer Society

2022
[49]

Jane Cleland-Huang, Orlena C. Z. Gotel, Jane Huffman Hayes, Patrick M\" a der, and Andrea Zisman. Software traceability: Trends and future directions. In Proceedings of the on Future of Software Engineering , FOSE'14, pages 55--69. ACM, 2014

2014
[50]

Software and Systems Traceability

Jane Cleland-Huang, Orlena Gotel, and Andrea Zisman. Software and Systems Traceability . Springer Publishing Company, Incorporated, 2012

2012
[51]

Achieving lightweight trustworthy traceability

Jane Cleland-Huang, Mona Rahimi, and Patrick M\" a der. Achieving lightweight trustworthy traceability. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering , FSE'14, pages 849--852, 2014

2014
[52]

Sequencer: Sequence-to-sequence learning for end-to-end program repair

Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, and Martin Monperrus. Sequencer: Sequence-to-sequence learning for end-to-end program repair. IEEE Transactions on Software Engineering , 47(9):1943--1959, 2021

1943
[53]

Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, and Alexey Svyatkovskiy

Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, and Alexey Svyatkovskiy. Long-range modeling of source code files with eWASH : Extended window access by syntax hierarchy
[54]

Bigquery: Serverless, highly scalable, and cost-effective multi-cloud data warehouse, 2025

Google Cloud. Bigquery: Serverless, highly scalable, and cost-effective multi-cloud data warehouse, 2025. Accessed: 2025-04-07

2025
[55]

Debugging tool for code generation neural language models, March 2024

Colin Bruce Clement, David Alberto Nader Palacio, Neelakantan Sundaresan, Alexey Svyatkovskiy, and Michele Tufano. Debugging tool for code generation neural language models, March 2024. Application No. 18/082366, filed on December 15, 2022

2024
[56]

Cisco Systems

Inc. Cisco Systems. Libest: Enrollment over secure transport (est) library, 2013. Accessed: 2025-04-07

2013
[57]

Improving smart contract security with contrastive learning-based vulnerability detection

Yizhou Chen, Zeyu Sun, Zhihao Gong, and Dan Hao. Improving smart contract security with contrastive learning-based vulnerability detection. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , ICSE '24, New York, NY, USA, 2024. Association for...

2024
[58]

What Makes a Good Explanation ?: A Harmonized View of Properties of Explanations , December 2022

Zixi Chen, Varshini Subhash, Marton Havasi, Weiwei Pan, and Finale Doshi-Velez. What Makes a Good Explanation ?: A Harmonized View of Properties of Explanations , December 2022. arXiv:2211.05667 [cs]

2022 arXiv
[59]

Snopy: Bridging sample denoising with causal graph learning for effective vulnerability detection

Sicong Cao, Xiaobing Sun, Xiaoxue Wu, David Lo, Lili Bo, Bin Li, Xiaolei Liu, Xingwei Lin, and Wei Liu. Snopy: Bridging sample denoising with causal graph learning for effective vulnerability detection. In 2024 39th IEEE/ACM International Conference on Automated Software Engin...

2024
[60]

Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott G...

2021
[61]

On the Properties of Neural Machine Translation : Encoder - Decoder Approaches , October 2014

Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. On the Properties of Neural Machine Translation : Encoder - Decoder Approaches , October 2014. arXiv:1409.1259 [cs, stat]

2014 arXiv
[62]

A survey on open source software trustworthiness

Vieri del Bianco, Luigi Lavazza, Sandro Morasca, and Davide Taibi. A survey on open source software trustworthiness. IEEE Software , 28(5):67--75, 2011

2011
[63]

Knowledge neurons in pretrained transformers

Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, and Furu Wei. Knowledge neurons in pretrained transformers. CoRR , abs/2104.08696, 2021

2021 arXiv
[64]

Enhancing software traceability by automatically expanding corpora with relevant documentation

Tathagata Dasgupta, Mark Grechanik, Evan Moritz, Bogdan Dit, and Denys Poshyvanyk. Enhancing software traceability by automatically expanding corpora with relevant documentation . IEEE International Conference on Software Maintenance, ICSM , pages 320--329, 2013

2013
[65]

Technique integration for requirements assessment

Alex Dekhtyar, Jane Huffman Hayes, Senthil Karthikeyan Sundaram, Elizabeth Ashlee Holbrook, and Olga Dekhtyar. Technique integration for requirements assessment. Proceedings of the 15th IEEE International Requirements Engineering Conference , pages 141--150, 2007

2007
[66]

Automatic traceability link recovery via active learning

Tianlong Du, Guo hua Shen, Zhi qiu Huang, Yaoliang Yu, and De xiang Wu. Automatic traceability link recovery via active learning. Frontiers of Information Technology & Electronic Engineering , 21:1217 -- 1225, 2020

2020
[67]

Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zha...

2024
[68]

Incremental approach and user feedbacks: a silver bullet for traceability recovery

Andrea De Lucia, Rocco Oliveto, and Paola Sgueglia. Incremental approach and user feedbacks: a silver bullet for traceability recovery. In Proceedings of the International Conference on Software Maintenance , ICSM'06, pages 299--309, 2006

2006
[69]

Ir-based traceability recovery processes: An empirical comparison of one-shot and incremental processes

Andrea De Lucia, Rocco Oliveto, and Genoveffa Tortora. Ir-based traceability recovery processes: An empirical comparison of one-shot and incremental processes. In Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering , pages 39--48. I...

2008
[70]

Assessing ir-based traceability recovery tools through controlled experiments

Andrea De Lucia, Rocco Oliveto, and Genoveffa Tortora. Assessing ir-based traceability recovery tools through controlled experiments. Empirical Software Engineering , 14(1):57--92, 2009

2009
[71]

Supporting and accelerating reproducible research in software maintenance using tracelab component library

Bogdan Dit, Evan Moritz, Mario Linares-V \' a squez, and Denys Poshyvanyk. Supporting and accelerating reproducible research in software maintenance using tracelab component library . IEEE International Conference on Software Maintenance, ICSM , pages 330--339, 2013

2013
[72]

Configuring topic models for software engineering tasks in tracelab

Bogdan Dit, Annibale Panichella, Evan Moritz, Rocco Oliveto, Massimilano Di Penta, Denys Poshyvanyk, and Andrea De Lucia. Configuring topic models for software engineering tasks in tracelab. In 2013 7th International Workshop on Traceability in Emerging Forms of Software Engin...

2013
[73]

Integrating information retrieval, execution and link analysis algorithms to improve feature location in software

Bogdan Dit, Meghan Revelle, and Denys Poshyvanyk. Integrating information retrieval, execution and link analysis algorithms to improve feature location in software . Empirical Software Engineering , 18(2):277--309, 2013

2013
[74]

What is artificial life today, and where should it go? Artificial Life , 30(1):1--15, 02 2024

Alan Dorin and Susan Stepney. What is artificial life today, and where should it go? Artificial Life , 30(1):1--15, 02 2024

2024
[75]

Gritsenko, Zhe Zhao, Neil Houlsby, Fernando Diaz, Donald Metzler, and Oriol Vinyals

Mostafa Dehghani, Yi Tay, Alexey A. Gritsenko, Zhe Zhao, Neil Houlsby, Fernando Diaz, Donald Metzler, and Oriol Vinyals. The benchmark lottery. CoRR , abs/2107.07002, 2021

2021 arXiv
[76]

Towards a rigorous science of interpretable machine learning, 2017

Finale Doshi-Velez and Been Kim. Towards a rigorous science of interpretable machine learning, 2017

2017
[77]

Considerations for Evaluation and Generalization in Interpretable Machine Learning , pages 3--17

Finale Doshi-Velez and Been Kim. Considerations for Evaluation and Generalization in Interpretable Machine Learning , pages 3--17. Springer International Publishing, Cham, 2018

2018
[78]

Eick, T.L

S.G. Eick, T.L. Graves, A.F. Karr, J.S. Marron, and A. Mockus. Does code decay? assessing the evidence from change management data. IEEE Transactions on Software Engineering , 27(1):1--12, 2001

2001
[79]

Hadeel Eladawy, Claire Le Goues, and Yuriy Brun. Automated program repair, what is it good for? not absolutely nothing! In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , ICSE '24, New York, NY, USA, 2024. Association for Computing Machinery

2024
[80]

Estimating the number of remaining links in traceability recovery

Davide Falessi, Massimiliano Di Penta, Gerardo Canfora, and Giovanni Cantone. Estimating the number of remaining links in traceability recovery. Empirical Software Engineering , 22(3):996--1027, 2017

2017
[81]

Furia, Robert Feldt, and Richard Torkar

Carlo A. Furia, Robert Feldt, and Richard Torkar. Bayesian data analysis in empirical software engineering research. IEEE Transactions on Software Engineering , abs/1811.05422, 2019

2019 arXiv
[82]

Automated repair of programs from large language models

Zhiyu Fan, Xiang Gao, Martin Mirchev, Abhik Roychoudhury, and Shin Hwei Tan. Automated repair of programs from large language models. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) , pages 1469--1481, 2023

2023
[83]

C ode BERT : A pre-trained model for programming and natural languages

Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. C ode BERT : A pre-trained model for programming and natural languages. In Trevor Cohn, Yulan He, and Yang Liu, editors, Findings of the Associ...

2020
[84]

Vulexplainer: A transformer-based hierarchical distillation for explaining vulnerability types

Michael Fu, Van Nguyen, Chakkrit Kla Tantithamthavorn, Trung Le, and Dinh Phung. Vulexplainer: A transformer-based hierarchical distillation for explaining vulnerability types. IEEE Transactions on Software Engineering , 49(10):4550--4565, 2023

2023
[85]

Comparing explanation methods for traditional machine learning models part 1: An overview of current methods and quantifying their disagreement

Montgomery Flora, Corey Potvin, Amy McGovern , and Shawn Handler. Comparing explanation methods for traditional machine learning models part 1: An overview of current methods and quantifying their disagreement
[86]

Can cooperative multi-agent reinforcement learning boost automatic web testing? an exploratory study

Yujia Fan, Sinan Wang, Zebang Fei, Yao Qin, Huaxuan Li, and Yepang Liu. Can cooperative multi-agent reinforcement learning boost automatic web testing? an exploratory study. In 2024 39th IEEE/ACM International Conference on Automated Software Engineering (ASE) , pages 14--26, 2024

2024
[87]

Trace++: A traceability approach to support transitioning to agile software engineering

Felipe Furtado and Andrea Zisman. Trace++: A traceability approach to support transitioning to agile software engineering. In Requirements Engineering Conference (RE), 2016 IEEE 24th International , pages 66--75. IEEE, 2016

2016
[88]

The pile: An 800gb dataset of diverse text for language modeling, 2020

Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. The pile: An 800gb dataset of diverse text for language modeling, 2020

2020
[89]

Codeparrot, 2021

Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. Codeparrot, 2021

2021
[90]

Semantically enhanced software traceability using deep learning techniques

Jin Guo, Jinghui Cheng, and Jane Cleland-Huang. Semantically enhanced software traceability using deep learning techniques. In Proceedings of the 39th International Conference on Software Engineering , ICSE'17, pages 3--14. IEEE Press, 2017

2017
[91]

Foundations for an expert system in domain-specific traceability

Jin Guo, Jane Cleland-Huang, and Brian Berenbach. Foundations for an expert system in domain-specific traceability. In 2013 21st IEEE International Requirements Engineering Conference (RE) , pages 42--51, 2013

2013
[92]

Reconciling Manual and Automatic Refactoring

Xi Ge, Quinton L Dubose, and Emerson Murphy-Hill. Reconciling Manual and Automatic Refactoring . In 2012 34th International Conference on Software Engineering (ICSE) , pages 211 -- 221, Zurich, 2012. IEEE

2012
[93]

Github, 2020

github. Github, 2020

2020
[94]

Github copilot, 2025

GitHub . Github copilot, 2025. Accessed: 2025-04-07

2025
[95]

Gethers, R

M. Gethers, R. Oliveto, D. Poshyvanyk, and A. D. Lucia. On integrating orthogonal information retrieval methods to improve traceability recovery. In Proceedings of the International Conference on Software Maintenance , ICSM'11, pages 133--142, 2011

2011
[96]

On integrating orthogonal information retrieval methods to improve traceability recovery

Malcom Gethers, Rocco Oliveto, Denys Poshyvanyk, and Andrea De Lucia. On integrating orthogonal information retrieval methods to improve traceability recovery. In 2011 27th IEEE International Conference on Software Maintenance (ICSM) , pages 133--142, 2011

2011
[97]

Cold-start Software Analytics

Jin Guo, Mona Rahimi, Jane Cleland-Huang, Alexander Rasin, Jane Huffman Hayes, and Michael Vierhauser. Cold-start Software Analytics . In Proceedings of the 13th International Conference on Mining Software Repositories , MSR '16, pages 142--153, Austin, Texas, 2016. ACM

2016
[98]

Graphcode \ bert \ : Pre-training code representations with data flow

Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie LIU, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. Graphcode \ bert \ : Pre-training code rep...

2021
[99]

Traceability recovery between bug reports and test cases-a mozilla firefox case study

Guilherme Gadelha, Franklin Ramalho, and Tiago Lima Massoni. Traceability recovery between bug reports and test cases-a mozilla firefox case study. Automated Software Engineering , 28, 2021

2021
[100]

Do automatic test generation tools generate flaky tests?, 2023

Martin Gruber, Muhammad Firhard Roslan, Owain Parry, Fabian Scharnböck, Phil McMinn, and Gordon Fraser. Do automatic test generation tools generate flaky tests?, 2023

2023
[101]

Towards automatic concept-based explanations

Amirata Ghorbani, James Wexler, James Zou, and Been Kim. Towards automatic concept-based explanations . Curran Associates Inc., Red Hook, NY, USA, 2019

2019
[102]

Deep code search

Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. Deep code search. In Proceedings of the 40th International Conference on Software Engineering , ICSE '18, pages 933--944, New York, NY, USA, 2018. ACM

2018
[103]

Measuring coding challenge competence with apps

Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt. Measuring coding challenge competence with apps. In J. Vanschoren and S. Yeung, editors, Proceedings of the Neural Inf...

2021
[104]

Barr, Zhendong Su, Mark Gabel, and Premkumar Devanbu

Abram Hindle, Earl T. Barr, Zhendong Su, Mark Gabel, and Premkumar Devanbu. On the naturalness of software. In Proceedings of the 34th International Conference on Software Engineering , ICSE '12, page 837–847. IEEE Press, 2012

2012
[105]

Hellendoorn and Premkumar Devanbu

Vincent J. Hellendoorn and Premkumar Devanbu. Are deep neural networks the best choice for modeling source code? ESEC/FSE 2017: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering , pages 763--773, 2017

2017
[106]

Advancing candidate link generation for requirements tracing: The study of methods

Jane Huffman Hayes, Alex Dekhtyar, and Senthil Karthikeyan Sundaram. Advancing candidate link generation for requirements tracing: The study of methods. IEEE Transactions on Software Engineering , 32(1):4, 2006

2006
[107]

Homan and Andrew Gelman

Matthew D. Homan and Andrew Gelman. The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo. J. Mach. Learn. Res. , 15(1):1593–1623, January 2014

2014
[108]

Deep transfer learning for source code modeling

Yasir Hussain, Zhiqiu Huang, Yu Zhou, and Senzhang Wang. Deep transfer learning for source code modeling. Int. J. Softw. Eng. Knowl. Eng. , 30:649--668, 2020

2020
[109]

Deep code comment generation

Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin. Deep code comment generation. In Proceedings of the 26th Conference on Program Comprehension , ICPC '18, page 200–210, New York, NY, USA, 2018. Association for Computing Machinery

2018
[110]

Hopcroft, Rajeev Motwani, and Jeffrey D

John E. Hopcroft, Rajeev Motwani, and Jeffrey D. Ullman. Introduction to Automata Theory, Languages, and Computation (3rd Edition) . Addison-Wesley Longman Publishing Co., Inc., USA, 2006

2006
[111]

Afshin Mansouri, and Yuanyuan Zhang

Mark Harman, S. Afshin Mansouri, and Yuanyuan Zhang. Search-based software engineering: Trends, techniques and applications. ACM Comput. Surv. , 45(1), December 2012

2012
[112]

Which explanation should i choose? a function approximation perspective to characterizing post hoc explanations

Tessa Han, Suraj Srinivas, and Himabindu Lakkaraju. Which explanation should i choose? a function approximation perspective to characterizing post hoc explanations
[113]

Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Hanchi Sun, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P....

2024
[114]

Neuron dependency graphs: A causal abstraction of neural networks

Yaojie Hu and Jin Tian. Neuron dependency graphs: A causal abstraction of neural networks. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning , volume 162...

2022
[115]

Codeparrot(codeparrot)

Hugginface. Codeparrot(codeparrot). Accessed: 23 July 2024

2024
[116]

CodeSearchNet challenge: Evaluating the state of semantic code search

Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. CodeSearchNet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436 , 2019

1909 arXiv
[117]

R. G. James, C. J. Ellison, and J. P. Crutchfield. dit : a P ython package for discrete information theory. The Journal of Open Source Software , 3(25):738, 2018

2018
[118]

Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?, 2020

Alon Jacovi and Yoav Goldberg. Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?, 2020

2020
[119]

Incremental latent semantic indexing for automatic traceability link evolution management

Hsin-Yi Jiang, Tien N Nguyen, Xiang Chen, Hojun Jaygarl, and Carl K Chang. Incremental latent semantic indexing for automatic traceability link evolution management. In Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering , ASE'08, p...

2008
[120]

Ai alignment: A comprehensive survey, 2025

Jiaming Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Lukas Vierling, Donghai Hong, Jiayi Zhou, Zhaowei Zhang, Fanzhi Zeng, Juntao Dai, Xuehai Pan, Kwan Yee Ng, Aidan O'Gara, Hua Xu, Brian Tse, Jie Fu, Stephen McAleer, Yaodong Yang...

2025
[121]

Sarthak Jain and Byron C. Wallace. Attention is not Explanation , May 2019. arXiv:1902.10186 [cs]

2019 arXiv
[122]

Tabnine: Ai code assistant, 2018

Jacob Jackson, Dror Weiss, and Eran Yahav. Tabnine: Ai code assistant, 2018. AI code assistant that accelerates and simplifies software development while keeping code private, secure, and compliant

2018
[123]

Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan

Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. Swe-bench: Can language models resolve real-world github issues?, 2024

2024
[124]

Kingma and Jimmy Ba

Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR , abs/1412.6980, 2015

2015 arXiv
[125]

Big code != big vocabulary: Open-vocabulary models for source code

Rafael Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, and Andrea Janes. Big code != big vocabulary: Open-vocabulary models for source code . Proceedings - International Conference on Software Engineering , pages 1073--1085, 2020

2020
[126]

Open-Vocabulary Models for Source Code (Extended Abstract)

Rafael Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, and Andrea Janes. Open-Vocabulary Models for Source Code (Extended Abstract) . Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering: Companion, ICSE-Companion 2020 , pages 294--...

2020
[127]

Jenkins: The leading open source automation server, 2025

Kohsuke Kawaguchi and Jenkins Community. Jenkins: The leading open source automation server, 2025. Accessed: 2025-04-07

2025
[128]

Decomposing information into copying versus transformation

Artemy Kolchinsky and Bernat Corominas-Murtra. Decomposing information into copying versus transformation . Journal of the Royal Society Interface , 17(162):1--17, 2020

2020
[129]

To ship or not to ship: An extensive evaluation of automatic metrics for machine translation

Tom Kocmi, Christian Federmann, Roman Grundkiewicz, Marcin Junczys - Dowmunt, Hitokazu Matsushita, and Arul Menezes. To ship or not to ship: An extensive evaluation of automatic metrics for machine translation. CoRR , abs/2107.10821, 2021

2021 arXiv
[130]

Traceclipse: an eclipse plug-in for traceability link recovery and management

Samuel Klock, Malcom Gethers, Bogdan Dit, and Denys Poshyvanyk. Traceclipse: an eclipse plug-in for traceability link recovery and management. In Proceedings of the international workshop on traceability in emerging forms of software engineering , TEFSE'11, pages 24--30, 2011

2011
[131]

Sharp nearby, fuzzy far away: How neural language models use context

Urvashi Khandelwal, He He, Peng Qi, and Dan Jurafsky. Sharp nearby, fuzzy far away: How neural language models use context. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 284--294, Melbourne, Australia...

2018
[132]

Visualizing and understanding recurrent networks

Andrej Karpathy, Justin Johnson, and Fei - Fei Li. Visualizing and understanding recurrent networks. CoRR , abs/1506.02078, 2015

2015 arXiv
[133]

Palacio, Yixuan Zhang, and Denys Poshyvanyk

Dipin Khati, Yijin Liu, David N. Palacio, Yixuan Zhang, and Denys Poshyvanyk. Mapping the trust terrain: Llms in software engineering -- insights and perspectives, 2025

2025
[134]

Robust Statistical Methods for Empirical Software Engineering

Barbara Kitchenham, Lech Madeyski, David Budgen, Jacky Keung, Pearl Brereton, Stuart Charters, Shirley Gibbs, and Amnart Pohthong. Robust Statistical Methods for Empirical Software Engineering . Empirical Software Engineering , 22(2):579--630, 2017

2017
[135]

On the relationship between explanation and prediction: A causal view

Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, and Been Kim. On the relationship between explanation and prediction: A causal view
[136]

u , Alexander Egyed, and Patrick M \

Hongyu Kuang, Jia Nie, Hao Hu, Patrick Rempel, Jian L \"u , Alexander Egyed, and Patrick M \"a der. Analyzing closeness of code dependencies for improving ir-based traceability recovery. In Proceedings of the 24th International Conference on Software Analysis, Evolution and Re...

2017
[137]

Sentencepiece: A simple and language-independent subword tokenizer and detokenizer for neural text processing

Taku Kudo and John Richardson. Sentencepiece: A simple and language-independent subword tokenizer and detokenizer for neural text processing. https://github.com/google/sentencepiece, 2024. Accessed: 2024-11-21

2024
[138]

On weighted shapley values

Ehud Kalai and Dov Samet. On weighted shapley values. International Journal of Game Theory , 16:205--222, 1983

1983
[139]

Maybe deep neural networks are the best choice for modeling source code, 2019

Rafael-Michael Karampatsis and Charles Sutton. Maybe deep neural networks are the best choice for modeling source code, 2019

2019
[140]

Interpretability Beyond Feature Attribution : Quantitative Testing with Concept Activation Vectors ( TCAV ), June 2018

Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. Interpretability Beyond Feature Attribution : Quantitative Testing with Concept Activation Vectors ( TCAV ), June 2018. arXiv:1711.11279 [stat]

2018 arXiv
[141]

Galeras benchmark: Benchmarking causal analysis for interpreting llms for source code

SEMERU Lab. Galeras benchmark: Benchmarking causal analysis for interpreting llms for source code. https://github.com/WM-SEMERU/galeras-benchmark, 2025. Accessed: 2025-04-07

2025
[142]

Christopher G. Langton. Artificial life: The proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems. In Artificial Life , volume VI of SFI Studies in the Sciences of Complexity , Redwood City, CA, 1989. Addison-Wesley

1989
[143]

Starcoder: may the source be with you!, 2023

Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, ...

2023
[144]

Improving trace accuracy through data-driven configuration and composition of tracing features

Sugandha Lohar, Sorawit Amornborvornwong, Andrea Zisman, and Jane Cleland-Huang. Improving trace accuracy through data-driven configuration and composition of tracing features. In Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering , ESEC/FSE 2013,...

2013
[145]

Manning, Christopher Ré, Diana Acosta-Navas, Drew A

Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas...
[146]

Ensemble Models for Neural Source Code Summarization of Subroutines , July 2021

Alexander LeClair, Aakash Bansal, and Collin McMillan. Ensemble Models for Neural Source Code Summarization of Subroutines , July 2021. arXiv:2107.11423 [cs]

2021 arXiv
[147]

Improving chatgpt prompt for code generation, 2023

Chao Liu, Xuanlin Bao, Hongyu Zhang, Neng Zhang, Haibo Hu, Xiaohong Zhang, and Meng Yan. Improving chatgpt prompt for code generation, 2023

2023
[148]

Gershman, and Finale Doshi-Velez

Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J. Gershman, and Finale Doshi-Velez. Human Evaluation of Models Built for Interpretability . Proceedings of the AAAI Conference on Human Computation and Crowdsourcing , 7:59--67, October 2019

2019
[149]

Programs, Life Cycles, and Laws of Software Evolution

Meir Lehman. Programs, Life Cycles, and Laws of Software Evolution . Proceedings of the IEEE , 1980

1980
[150]

De Lucia, F

A. De Lucia, F. Fasano, R. Oliveto, and G. Tortora. Enhancing an artefact management system with traceability recovery features. In Proceedings of the 20th IEEE International Conference on Software Maintenance, 2004. Proceedings. , ICSM'04, pages 306--315, Sept 2004

2004
[151]

Vera Liao, Daniel Gruen, and Sarah Miller

Q. Vera Liao, Daniel Gruen, and Sarah Miller. Questioning the ai: Informing design practices for explainable ai user experiences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems , CHI '20, page 1–15, New York, NY, USA, 2020. Association for Comp...

2020
[152]

Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and S...

2021 arXiv
[153]

Zachary C. Lipton. The mythos of model interpretability
[154]

Lundberg and Su-In Lee

Scott M. Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems , NIPS'17, page 4768–4777, Red Hook, NY, USA, 2017. Curran Associates Inc

2017
[155]

Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension, 2019

Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension, 2019

2019
[156]

Traceability transformed: Generating more accurate links with pre-trained bert models

Jinfeng Lin, Yalin Liu, Qingkai Zeng, Meng Jiang, and Jane Cleland-Huang. Traceability transformed: Generating more accurate links with pre-trained bert models. In Proceedings of the 43rd International Conference on Software Engineering , ICSE '21, page 324–335. IEEE Press, 2021

2021
[157]

Trustworthy and synergistic artificial intelligence for software engineering: Vision and roadmaps

David Lo. Trustworthy and synergistic artificial intelligence for software engineering: Vision and roadmaps
[158]

Luca Longo, editor. Explainable Artificial Intelligence: First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I , volume 1901 of Communications in Computer and Information Science . Springer, 2023

2023
[159]

Explainable ai: A review of machine learning interpretability methods

Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis. Explainable ai: A review of machine learning interpretability methods. Entropy , 23(1), 2021

2021
[160]

Poudel, Wenhao Yu, Qingkai Zeng, Meng Jiang, and Jane Cleland-Huang

Jinfeng Lin, A. Poudel, Wenhao Yu, Qingkai Zeng, Meng Jiang, and Jane Cleland-Huang. Enhancing automated software traceability by transfer learning from open-world data. ArXiv , abs/2207.01084, 2022

2022 arXiv
[161]

Lee and Katrina A

John D. Lee and Katrina A. See. Trust in automation: Designing for appropriate reliance. Human Factors , 46(1):50--80, 2004. PMID: 15151155

2004
[162]

Clacer: A deep learning-based compilation error classification method for novice students’ programs

Zheng Li, Fuxiang Sun, Haifeng Wang, Yifan Ding, Yong Liu, and Xiang Chen. Clacer: A deep learning-based compilation error classification method for novice students’ programs. In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) , pages 74--83, 2021

2021
[163]

No need to lift a finger anymore? assessing the quality of code generation by chatgpt, 2023

Zhijie Liu, Yutian Tang, Xiapu Luo, Yuming Zhou, and Liang Feng Zhang. No need to lift a finger anymore? assessing the quality of code generation by chatgpt, 2023

2023
[164]

Yue Liu, Chakkrit Tantithamthavorn, Li Li, and Yepang Liu. Explainable ai for android malware detection: Towards understanding why the models perform so well? In 2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE) , pages 169--180, 2022

2022
[165]

On the reliability and explainability of language models for program generation

Yue Liu, Chakkrit Tantithamthavorn, Yonghui Liu, and Li Li. On the reliability and explainability of language models for program generation. ACM Trans. Softw. Eng. Methodol. , 33(5), June 2024

2024
[166]

AST - Probe : Recovering abstract syntax trees from hidden representations of pre-trained language models, September 2022

José Antonio Hernández López, Martin Weyssow, Jesús Sánchez Cuadrado, and Houari Sahraoui. AST - Probe : Recovering abstract syntax trees from hidden representations of pre-trained language models, September 2022. arXiv:2206.11719 [cs]

2022 arXiv
[167]

D. Li , Z. Wang , and Y. Xue . Fine-grained android malware detection based on deep learning. In 2018 IEEE Conference on Communications and Network Security (CNS) , pages 1--2, May 2018

2018
[168]

Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation, 2023

Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation, 2023

2023
[169]

Deep learning based feature envy detection

Hui Liu, Zhifeng Xu, and Yanzhen Zou. Deep learning based feature envy detection. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering , ASE 2018, pages 385--396, New York, NY, USA, 2018. ACM

2018
[170]

Trustworthy llms: a survey and guideline for evaluating large language models' alignment, 2024

Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo, Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, and Hang Li. Trustworthy llms: a survey and guideline for evaluating large language models' alignment, 2024

2024
[171]

Do pretrained language models indeed understand software engineering tasks? IEEE Transactions on Software Engineering , 49(10):4639--4655, 2023

Yao Li, Tao Zhang, Xiapu Luo, Haipeng Cai, Sen Fang, and Dawei Yuan. Do pretrained language models indeed understand software engineering tasks? IEEE Transactions on Software Engineering , 49(10):4639--4655, 2023

2023
[172]

P. Liu , X. Zhang , M. Pistoia , Y. Zheng , M. Marques , and L. Zeng . Automatic text input generation for mobile testing. In 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) , pages 643--653, May 2017

2017
[173]

David J. C. MacKay. Information theory, inference, and learning algorithms . Cambridge University Press, 2003

2003
[174]

Autopoesis and Cognition

Varela Francisco Maturana, Humberto. Autopoesis and Cognition . Springer, 1980

1980
[175]

Better automatic program repair by using bug reports and tests together

Manish Motwani and Yuriy Brun. Better automatic program repair by using bug reports and tests together. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) , pages 1225--1237, May 2023

2023
[176]

Interpretable machine learning – a brief history, state-of-the-art and challenges

Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl. Interpretable machine learning – a brief history, state-of-the-art and challenges. In Mohammed J. Zaki, Panagiotis Papapetrou, Victor S. Sheng, Aristides Gionis, Lars Schmidt-Thieme, and Wolfgang J. Krause, editors, ECM...

2020
[177]

Automatic Traceability Maintenance via Machine Learning Classification

Chris Mills, Javier Escobar-Avila, and Sonia Haiduc. Automatic Traceability Maintenance via Machine Learning Classification . In Proceedings of the 34th IEEE International Conference on Software Maintenance and Evolution , ICSME'18, pages 369--380, Madrid, Spain, September 2018. ACM

2018
[178]

Mac \' i as-Escriv \' a , Rodolfo Haber, Raul Del Toro, and Vicente Hernandez

Frank D. Mac \' i as-Escriv \' a , Rodolfo Haber, Raul Del Toro, and Vicente Hernandez. Self-adaptive systems: A survey of current approaches, research challenges and applications . Expert Systems with Applications , 40(18):7267--7279, 2013

2013
[179]

Data mining static code attributes to learn defect predictors

Tim Menzies, Jeremy Greenwald, and Art Frank. Data mining static code attributes to learn defect predictors. IEEE Transactions on Software Engineering , 33(1):2--13, 2007

2007
[180]

Visual studio intellicode, 2025

Microsoft . Visual studio intellicode, 2025. Accessed: 2025-04-07

2025
[181]

Explanation in artificial intelligence: Insights from the social sciences

Tim Miller. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence , 267:1--38, 2019

2019
[182]

Causal inference and discovery in Python: unlock the secrets of modern causal machine learning with DoWhy , EconML , PyTorch and more

Aleksander Molak and Ajit Jaokar. Causal inference and discovery in Python: unlock the secrets of modern causal machine learning with DoWhy , EconML , PyTorch and more . Packt Publishing Limited, 2023

2023
[183]

Deepgauge: Multi-granularity testing criteria for deep learning systems

Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, Jianjun Zhao, and Yadong Wang. Deepgauge: Multi-granularity testing criteria for deep learning systems. In Proceedings of the 33rd ACM/IEEE International Conference o...

2018
[184]

M\" a der, P

P. M\" a der, P. L. Jones, Y. Zhang, and J. Cleland-Huang. Strategic traceability for safety-critical projects. IEEE Software , 30(3):58--66, May 2013

2013
[185]

A robust multi-objective approach to balance severity and importance of refactoring opportunities , 2016

Mohamed Wiem Mkaouer, Marouane Kessentini, Mel Cinn \' e ide, Shinpei Hayashi, and Kalyanmoy Deb. A robust multi-objective approach to balance severity and importance of refactoring opportunities , 2016

2016
[186]

Murphy, Mik Kersten, and Leah Findlater

Gail C. Murphy, Mik Kersten, and Leah Findlater. How are java software developers using the eclipse ide? IEEE Softw. , 23(4):76–83, July 2006

2006
[187]

Causal interpretability for machine learning -- problems, methods and evaluation, 2020

Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, and Huan Liu. Causal interpretability for machine learning -- problems, methods and evaluation, 2020

2020
[188]

James Murdoch, Peter J

W. James Murdoch, Peter J. Liu, and Bin Yu. Beyond word importance: Contextual decomposition to extract interactions from LSTM s. In International Conference on Learning Representations , 2018

2018
[189]

Andrian Marcus and Jonathan I. Maletic. Recovering documentation-to-source-code traceability links using latent semantic indexing. In Proceedings of the 25th International Conference on Software Engineering , ICSE '03, pages 125--135, Washington, DC, USA, 2003. IEEE Computer Society

2003
[190]

Khapra, Balaji Vasan Srinivasan, and Balaraman Ravindran

Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan, Mitesh M. Khapra, Balaji Vasan Srinivasan, and Balaraman Ravindran. Towards Transparent and Explainable Attention Models . In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , page...

2020
[191]

Mahmoud, N

A. Mahmoud, N. Niu, and S. Xu. A semantic relatedness approach for traceability link recovery. In Proceedings of the 20th IEEE International Conference on Program Comprehension , ICPC'12, pages 183--192, June 2012

2012
[192]

Interpretable Machine Learning

Christoph Molnar. Interpretable Machine Learning . 3 edition, 2025

2025
[193]

User experience design, 2004

Peter Morville. User experience design, 2004. Accessed: 2025-04-07

2004
[194]

Reviewing the landscape: Component-based software engineering practices and challenges

Pradeep Kumar Mahapatro and Neelamadhab Padhy. Reviewing the landscape: Component-based software engineering practices and challenges. In 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC) , pages 360--365, 2024

2024
[195]

Palacio, Carlos Bernal-C\' a rdenas, Daniel McCrystal, Denys Poshyvanyk, Chris Shenefiel, and Jeff Johnson

Kevin Moran, David N. Palacio, Carlos Bernal-C\' a rdenas, Daniel McCrystal, Denys Poshyvanyk, Chris Shenefiel, and Jeff Johnson. Improving the effectiveness of traceability link recovery using hierarchical bayesian networks. In Proceedings of the ACM/IEEE 42nd International C...

2020
[196]

improving the effectiveness of traceability link recovery using hierarchical bayesian networks

Kevin Moran, David N. Palacio, Carlos Bernal-Cardenas, Daniel McCrystal, Denys Poshyvanyk, Chris Shenefiel, and Jeff Johnson. Online appendix for "improving the effectiveness of traceability link recovery using hierarchical bayesian networks", 2020. Accessed: 2025-04-07

2020
[197]

Combining textual and structural analysis of software artifacts for traceability link recovery

Collin McMillan, Denys Poshyvanyk, and Meghan Revelle. Combining textual and structural analysis of software artifacts for traceability link recovery. In Proceedings of the ICSE Workshop on Traceability in Emerging Forms of Software Engineering , TEFSE '09, pages 41--48. IEEE ...

2009
[198]

Studying the usage of text-to-text transfer transformer to support code-related tasks

Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader Palacio, Denys Poshyvanyk, Rocco Oliveto, and Gabriele Bavota. Studying the usage of text-to-text transfer transformer to support code-related tasks. In Proceedings of the 43rd International Conference on Softw...

2021
[199]

Ahmad Haji Mohammadkhani, Chakkrit Tantithamthavorn, and Hadi Hemmatif. Explaining transformer-based code models: What do they learn? when they do not work? In 2023 IEEE 23rd International Working Conference on Source Code Analysis and Manipulation (SCAM) , pages 96--106, 2023

2023
[200]

Kevin P. Murphy. Machine Learning: A Probabilistic Perspective . The MIT Press, 2012

2012
[201]

Detecting, classifying, and tracing non-functional software requirements

Anas Mahmoud and Grant Williams. Detecting, classifying, and tracing non-functional software requirements. Requirements Engineering , 21(3):357--381, Sep 2016

2016
[202]

An empirical investigation into the use of image captioning for automated software documentation

Kevin Moran, Ali Yachnes, George Purnell, Junayed Mahmud, Michele Tufano, Carlos Bernal Cardenas, Denys Poshyvanyk, and Zach H'Doubler. An empirical investigation into the use of image captioning for automated software documentation. In 2022 IEEE International Conference on So...

2022
[203]

The general and logical theory of automata

John Von Neumann. The general and logical theory of automata. In L.A. Jeffress, editor, Cerebral Mechanisms in Behaviour—The Hixon Symposium , pages 1--41, New York, NY, USA, 1951. Wiley

1951
[204]

Theory of Self-Reproducing Automata

John Von Neumann. Theory of Self-Reproducing Automata . University of Illinois Press, Champaign, IL, USA, 1966

1966
[205]

J. Neyman. Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability . Royal Society, 1937

1937
[206]

Anh Tuan Nguyen and Tien N. Nguyen. Graph-based statistical language model for code. In Proceedings of the 37th International Conference on Software Engineering - Volume 1 , ICSE '15, page 858–868. IEEE Press, 2015

2015
[207]

Nguyen, A

T. Nguyen, A. Nguyen, and H. Nguyen. A statistical semantic language model for source code. In ESEC/FSE 2013 , 2013

2013
[208]

Tung Thanh Nguyen, Anh Tuan Nguyen, Hoan Anh Nguyen, and Tien N. Nguyen. A statistical semantic language model for source code. In Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering , ESEC/FSE 2013, page 532–542, New York, NY, USA, 2013. Associati...

2013
[209]

Codegen: An open large language model for code with multi-turn program synthesis

Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. Codegen: An open large language model for code with multi-turn program synthesis. In The Eleventh International Conference on Learning Representations , 2023

2023
[210]

Assessing Single-Objective Performance Convergence and Time Complexity for Refactoring Detection

David Nader-palacio, Daniel Rodr \' i guez-c \' a rdenas, and Jonatan Gomez. Assessing Single-Objective Performance Convergence and Time Complexity for Refactoring Detection . In Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18 , page...

2018
[211]

A sysml-based approach to traceability management and design slicing in support of safety certification: Framework, tool support, and case studies

Shiva Nejati, Mehrdad Sabetzadeh, Davide Falessi, Lionel Briand, and Thierry Coq. A sysml-based approach to traceability management and design slicing in support of safety certification: Framework, tool support, and case studies. Inf. Softw. Technol. , 54(6):569--590, June 2012

2012
[212]

Recovering transitive traceability links among software artifacts

Kazuki Nishikawa, Hironori Washizaki, Yoshiaki Fukazawa, Keishi Oshima, and Ryota Mibe. Recovering transitive traceability links among software artifacts. In Proceedings of the IEEE International Conference on Software Maintenance and Evolution , ICSME'15, pages 576--580. IEEE, 2015

2015
[213]

Khan, and Bashar Nuseibeh

Armstrong Nhlabatsi, Yijun Yu, Andrea Zisman, Thein Tun, Niamul Khan, Arosha Bandara, Khaled M. Khan, and Bashar Nuseibeh. Managing security control assumptions using causal traceability. In Proceedings of the 8th International Symposium on Software and Systems Traceability , ...

2015
[214]

Barr, and Justyna Petke

Leandro Oliveria de Souza, Eduardo Santana de Almeida, Paulo Anselmo da Mota Silveira Neto, Earl T. Barr, and Justyna Petke. Software product line engineering via software transplantation. ACM Trans. Softw. Eng. Methodol. , 34(2), January 2025

2025
[215]

Coest traceability datasets, 2025

Center of Excellence for Software & Systems Traceability (CoEST). Coest traceability datasets, 2025. Accessed: 2025-04-07

2025
[216]

On the equivalence of information retrieval methods for automated traceability link recovery

Rocco Oliveto, Malcom Gethers, Denys Poshyvanyk, and Andrea De Lucia. On the equivalence of information retrieval methods for automated traceability link recovery. In Proceedings of the 18th International Conference on Program Comprehension , ICPC '10, pages 68--71, 2010

2010
[217]

Review of system development life cycle (sdlc) models for effective application delivery

Oluwaseyi Ezekiel Olorunshola and Francisca Nonyelum Ogwueleka. Review of system development life cycle (sdlc) models for effective application delivery. In Amit Joshi, Mufti Mahmud, Roshan G. Ragel, and Nileshsingh V. Thakur, editors, Information and Communication Technology ...

2020
[218]

Gpt-4 technical report, 2023

OpenAI. Gpt-4 technical report, 2023

2023
[219]

Java language keywords, 2024

Oracle. Java language keywords, 2024. Accessed: 2025-04-07

2024
[220]

David N. Palacio. Information theory for interpreting software traceability. https://danaderp.github.io/danaderp/projects/1_project/, 2023

2023
[221]

Transportability of causal and statistical relations: A formal approach

Judea Pearl and Elias Bareinboim. Transportability of causal and statistical relations: A formal approach. In 2011 IEEE 11th International Conference on Data Mining Workshops , pages 540--547, 2011

2011
[222]

Palacio, Nathan Cooper, Alvaro Rodriguez, Kevin Moran, and Denys Poshyvanyk

David N. Palacio, Nathan Cooper, Alvaro Rodriguez, Kevin Moran, and Denys Poshyvanyk. Toward a Theory of Causation for Interpreting Neural Code Models , February 2023. arXiv:2302.03788 [cs, stat]

2023 arXiv
[223]

Deep learning & software engineering: State of research and future directions

Devanbu Prem, Matthew Dwyer, Sebastian Elbaum, Michael Lowry, Kevin Moran, Denys Poshyvanyk, Baishakhi Ray, Rishabh Singh, and Xiangyu Zhang. Deep learning & software engineering: State of research and future directions. In Proceedings of the 2019 NSF Workshop on Deep Learning...

2019
[224]

Causal inference in statistics: An overview

Judea Pearl. Causal inference in statistics: An overview. Statistics Surveys , 3:96--146, 2009

2009
[225]

Causality: Models, Reasoning and Inference

Judea Pearl. Causality: Models, Reasoning and Inference . Cambridge University Press, USA, 2nd edition, 2009

2009
[226]

Theoretical impediments to machine learning with seven sparks from the causal revolution

Judea Pearl. Theoretical impediments to machine learning with seven sparks from the causal revolution. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining , WSDM '18, page 3, New York, NY, USA, 2018. Association for Computing Machinery

2018
[227]

Pytorch: An imperative style, high-performance deep learning library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu ...

2019
[228]

Causal Inference in Statistics, A Primer

Judea Pearl, Madelyn Glymour, and Nicholas P.Jewell. Causal Inference in Statistics, A Primer . Wiley, 2016

2016
[229]

Perturbation sensitivity analysis to detect unintended model biases

Vinodkumar Prabhakaran, Ben Hutchinson, and Margaret Mitchell. Perturbation sensitivity analysis to detect unintended model biases. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural L...

2019
[230]

Palacio, Dipin Khati, Daniel Rodriguez-Cardenas, Alejandro Velasco, and Denys Poshyvanyk

David N. Palacio, Dipin Khati, Daniel Rodriguez-Cardenas, Alejandro Velasco, and Denys Poshyvanyk. Codeq: On explaining (large) language models for code using global code-based explanations, 2025

2025
[231]

The Book of Why: The New Science of Cause and Effect

Judea Pearl and Dana Mackenzie. The Book of Why: The New Science of Cause and Effect . Basic Books, Inc., USA, 1st edition, 2018

2018
[232]

Palacio, Daniel McCrystal, Kevin Moran, Carlos Bernal-Cárdenas, Denys Poshyvanyk, and Chris Shenefiel

David N. Palacio, Daniel McCrystal, Kevin Moran, Carlos Bernal-Cárdenas, Denys Poshyvanyk, and Chris Shenefiel. Learning to identify security-related issues using convolutional neural networks. In 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)...

2019
[233]

Palacio, Daniel Rodriguez-Cardenas, Alejandro Velasco, Dipin Khati, Kevin Moran, and Denys Poshyvanyk

David N. Palacio, Daniel Rodriguez-Cardenas, Alejandro Velasco, Dipin Khati, Kevin Moran, and Denys Poshyvanyk. Astrust: Github repository, 2024

2024
[234]

Palacio, Daniel Rodriguez-Cardenas, Alejandro Velasco, Dipin Khati, Kevin Moran, and Denys Poshyvanyk

David N. Palacio, Daniel Rodriguez-Cardenas, Alejandro Velasco, Dipin Khati, Kevin Moran, and Denys Poshyvanyk. Towards more trustworthy and interpretable llms for code through syntax-grounded explanations, 2024

2024
[235]

BLEU : a method for automatic evaluation of machine translation

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. BLEU : a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics , ACL '02, pages 311--318, USA, July 2002. Association for Compu...

2002
[236]

Goldstein, Jake M

Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. Manipulating and Measuring Model Interpretability , August 2021. arXiv:1802.07810 [cs]

2021 arXiv
[237]

Pyexplainer: Explaining the predictions of just-in-time defect models

Chanathip Pornprasit, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Michael Fu, and Patanamon Thongtanunam. Pyexplainer: Explaining the predictions of just-in-time defect models. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) , pages 40...

2021
[238]

Qualtrics experience management platform, 2025

Qualtrics. Qualtrics experience management platform, 2025. Accessed: 2025-04-10

2025
[239]

Software evolution and maintenance

V\' a clav Rajlich. Software evolution and maintenance. In Future of Software Engineering Proceedings , FOSE 2014, page 133–144, New York, NY, USA, 2014. Association for Computing Machinery

2014
[240]

On the generalizability of neural program models with respect to semantic-preserving program transformations

Md Rafiqul Islam Rabin, Nghi DQ Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, and Mohammad Amin Alipour. On the generalizability of neural program models with respect to semantic-preserving program transformations. Information and Software Technology , 135:106552, 2021

2021
[241]

tracexplainer, 2024

RepoTrace. tracexplainer, 2024. [Accessed 11-24-2024]

2024
[242]

Codebleu: a method for automatic evaluation of code synthesis

Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma. Codebleu: a method for automatic evaluation of code synthesis. CoRR , abs/2009.10297, 2020

2009 arXiv
[243]

naturalness

Baishakhi Ray, Vincent Hellendoorn, Saheel Godhane, Zhaopeng Tu, Alberto Bacchelli, and Premkumar Devanbu. On the "naturalness" of buggy code. In Proceedings of the 38th International Conference on Software Engineering , ICSE '16, page 428–439, New York, NY, USA, 2016. Associa...

2016
[244]

Know what you don ' t know: Unanswerable questions for SQ u AD

Pranav Rajpurkar, Robin Jia, and Percy Liang. Know what you don ' t know: Unanswerable questions for SQ u AD . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , pages 784--789, Melbourne, Australia, July 2018....

2018
[245]

Unsupervised translation of programming languages

Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample. Unsupervised translation of programming languages. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems , volume 33, pages 206...

2020
[246]

Mind the Gap : Assessing the Conformance of Software Traceability to Relevant Guidelines

Patrick Rempel, Patrick Mader, Tobias Kuschke, and Jane Cleland-Huang. Mind the Gap : Assessing the Conformance of Software Traceability to Relevant Guidelines . In Proceedings of the 36th International Conference on Software Engineering , ICSE'14, pages 943--954, Hyderabad, I...

2014
[247]

Traceability in the wild: automatically augmenting incomplete trace links

Michael Rath, Jacob Rendall, Jin LC Guo, Jane Cleland-Huang, and Patrick M \"a der. Traceability in the wild: automatically augmenting incomplete trace links. In Proceedings of the 40th International Conference on Software Engineering , ICSE'18, pages 834--845. ACM, 2018

2018
[248]

Ra \" ffa and R

H. Ra \" ffa and R. Schlaifer. Applied statistical decision theory . Studies in managerial economics. Division of Research, Graduate School of Business Adminitration, Harvard University, 1961

1961
[249]

COMET : A neural framework for MT evaluation

Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. COMET : A neural framework for MT evaluation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages 2685--2702, Online, November 2020. Association for Computational L...

2020
[250]

why should i trust you?

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. "why should i trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD '16, page 1135–1144, New York, NY, USA, ...

2016
[251]

A Multi-study Investigation Into Dead Code

Simone Romano, Christopher Vendome, Giuseppe Scanniello, and Denys Poshyvanyk. A Multi-study Investigation Into Dead Code . IEEE Transactions on Software Engineering , 2018

2018
[252]

Vechev, and Eran Yahav

Veselin Raychev, Martin T. Vechev, and Eran Yahav. Code completion with statistical language models. Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation , 2014

2014
[253]

Beyond accuracy: Behavioral testing of NLP models with C heck L ist

Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. Beyond accuracy: Behavioral testing of NLP models with C heck L ist. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages 4902--4912, Online, July 2020. Associa...

2020
[254]

Desiderata for interpretability: Explaining decision tree predictions with counterfactuals

Kacper Sokol and Peter Flach. Desiderata for interpretability: Explaining decision tree predictions with counterfactuals. Proceedings of the AAAI Conference on Artificial Intelligence , 33:10035--10036, 07 2019

2019
[255]

Learning Important Features Through Propagating Activation Differences , October 2019

Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. Learning Important Features Through Propagating Activation Differences , October 2019. arXiv:1704.02685 [cs]

2019 arXiv
[256]

Dataflow analysis-inspired deep learning for efficient vulnerability detection

Benjamin Steenhoek, Hongyang Gao, and Wei Le. Dataflow analysis-inspired deep learning for efficient vulnerability detection. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , ICSE '24, New York, NY, USA, 2024. Association for Computing Machinery

2024
[257]

Calibration and correctness of language models for code, 2024

Claudio Spiess, David Gros, Kunal Suresh Pai, Michael Pradel, Md Rafiqul Islam Rabin, Amin Alipour, Susmit Jha, Prem Devanbu, and Toufique Ahmed. Calibration and correctness of language models for code, 2024

2024
[258]

Revising rules to capture requirements traceability relations: A machine learning approach

George Spanoudakis, Artur S d'Avila Garcez, and Andrea Zisman. Revising rules to capture requirements traceability relations: A machine learning approach. In SEKE , SEKE'03, pages 570--577, 2003

2003
[259]

Neural machine translation of rare words with subword units

Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909 , 2015

2015 arXiv
[260]

Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric Xing, Furong H...

2024
[261]

Do W hy: A Python package for causal inference

Amit Sharma, Emre Kiciman, et al. Do W hy: A Python package for causal inference . https://github.com/microsoft/dowhy, 2019

2019
[262]

Characterizing software maintenance meetings: Information shared, discussion outcomes, and information captured

Adriana Meza Soria, Taylor Lopez, Elizabeth Seero, Negin Mashhadi, Emily Evans, Janet Burge, and Andr\' e Van der Hoek. Characterizing software maintenance meetings: Information shared, discussion outcomes, and information captured. In Proceedings of the IEEE/ACM 46th Internat...

2024
[263]

A literature review of using machine learning in software development life cycle stages

Saad Shafiq, Atif Mashkoor, Christoph Mayr-Dorn, and Alexander Egyed. A literature review of using machine learning in software development life cycle stages. IEEE Access , 9:140896--140920, 2021

2021
[264]

Hiroki Sayama and Chrystopher L. Nehaniv. Self-reproduction and evolution in cellular automata: 25 years after evoloops. Artificial Life , 31(1):81–95, February 2024

2024
[265]

Explainable artificial intelligence (xai)

Management Solutions. Explainable artificial intelligence (xai). challenges of model interpretability. https://www.managementsolutions.com/sites/default/files/minisite/static/22959b0f-b3da-47c8-9d5c-80ec3216552b/iax/pdf/explainable-artificial-intelligence-en-04.pdf, 2022. Acce...

2022
[266]

What is usability? a characterization based on iso 9241-11 and iso/iec 25010, 2022

Maximilian Speicher. What is usability? a characterization based on iso 9241-11 and iso/iec 25010, 2022

2022
[267]

Jeffrey Svajlenko and Chanchal K. Roy. Evaluating clone detection tools with BigCloneBench . 2015 IEEE 31st International Conference on Software Maintenance and Evolution, ICSME 2015 - Proceedings , pages 131--140, 2015

2015
[268]

Sofia Serrano and Noah A. Smith. Is Attention Interpretable ?, June 2019. arXiv:1906.03731 [cs]

2019 arXiv
[269]

Dowhy: Addressing challenges in expressing and validating causal assumptions, 2021

Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, and Emre Kıcıman. Dowhy: Addressing challenges in expressing and validating causal assumptions, 2021

2021
[270]

Axiomatic attribution for deep networks

Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 , ICML'17, page 3319–3328. JMLR.org, 2017

2017
[271]

From statistical to causal learning, 2022

Bernhard Schölkopf and Julius von Kügelgen. From statistical to causal learning, 2022

2022
[272]

Probing Pretrained Models of Source Code , November 2022

Sergey Troshin and Nadezhda Chirkova. Probing Pretrained Models of Source Code , November 2022. arXiv:2202.08975 [cs]

2022 arXiv
[273]

Explainable ai for se: Challenges and future directions

Chakkrit Tantithamthavorn, Jürgen Cito, Hadi Hemmati, and Satish Chandra. Explainable ai for se: Challenges and future directions. IEEE Software , 40(3):29--33, 2023

2023
[274]

BERT rediscovers the classical NLP pipeline

Ian Tenney, Dipanjan Das, and Ellie Pavlick. BERT rediscovers the classical NLP pipeline. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages 4593--4601, Florence, Italy, July 2019. Association for Computational Linguistics

2019
[275]

Wikipedia dataset, 2025

TensorFlow Datasets . Wikipedia dataset, 2025. Accessed: 2025-04-07

2025
[276]

Theano: A Python framework for fast computation of mathematical expressions

Theano Development Team . Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints , abs/1605.02688, 2016

2016 arXiv
[277]

Understanding the interplay between trust, reliability, and human factors in the age of generative ai

Simon Thorne. Understanding the interplay between trust, reliability, and human factors in the age of generative ai. International Journal of Simulation: Systems, Science & technology , May 2024

2024
[278]

Using pre-trained models to boost code review automation

Rosalia Tufano, Simone Masiero, Antonio Mastropaolo, Luca Pascarella, Denys Poshyvanyk, and Gabriele Bavota. Using pre-trained models to boost code review automation. In 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) , pages 2291--2302, 2022

2022
[279]

and D Poshyvanyk

Watson C Bavota G Di Penta M White M Tufano M. and D Poshyvanyk. Deep Learning Similarities from Different Representations of Source Code . In Proceedings of the 15th IEEE/ACM Conference on Mining Software Repositories (MSR’18) , MSR '18, Gothenburg, Sweden, 2018

2018
[280]

When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away)

Michele Tufano, Fabio Palomba, Gabriele Bavota, Rocco Oliveto, Massimiliano Di Penta, Andrea De Lucia, and Denys Poshyvanyk. When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away) . IEEE Transactions on Software Engineering , 43(11):1063--1088, 2017

2017
[281]

Deeptest: Automated testing of deep-neural-network-driven autonomous cars

Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray. Deeptest: Automated testing of deep-neural-network-driven autonomous cars. In Proceedings of the 40th International Conference on Software Engineering , ICSE '18, pages 303--314, New York, NY, USA, 2018. ACM

2018
[282]

Towards automating code review activities

Rosalia Tufano, Luca Pascarella, Michele Tufano, Denys Poshyvanyk, and Gabriele Bavota. Towards automating code review activities. In 43rd International Conference on Software Engineering, ICSE '21 , 2021

2021
[283]

On learning meaningful code changes via neural machine translation

Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota, and Denys Poshyvanyk. On learning meaningful code changes via neural machine translation. In Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, Montreal, QC, Canada, May 25-3...

2019
[284]

On the localness of software

Zhaopeng Tu, Zhendong Su, and Premkumar Devanbu. On the localness of software. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering , FSE 2014, page 269–280, New York, NY, USA, 2014. Association for Computing Machinery

2014
[285]

Robert R. Tucci. Introduction to judea pearl's do-calculus, 2013

2013
[286]

Deep learning similarities from different representations of source code

Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. Deep learning similarities from different representations of source code. In 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR) , pages 542--...

2018
[287]

An empirical investigation into learning bug-fixing patches in the wild via neural machine translation

Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. An empirical investigation into learning bug-fixing patches in the wild via neural machine translation. In Proceedings of the 33rd ACM/IEEE International Conference on Auto...

2018
[288]

Learning How to Mutate Source Code from Bug-Fixes

Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. Learning How to Mutate Source Code from Bug-Fixes . ICSME 2019 , pages 301--312, 2019

2019
[289]

An empirical study on learning bug-fixing patches in the wild via neural machine translation

Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. An empirical study on learning bug-fixing patches in the wild via neural machine translation. ACM Trans. Softw. Eng. Methodol. , 28(4):19:1--19:29, 2019

2019
[290]

Vera Liao, and Jennifer Wortman Vaughan

Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, and Jennifer Wortman Vaughan. Generation probabilities are not enough: Uncertainty highlighting in ai code completions. ACM Trans. Comput.-Hum. Interact. , October 2024. Just Accepted

2024
[291]

Blei, and Alexander M

Keyon Vafa, Yuntian Deng, David M. Blei, and Alexander M. Rush. Rationales for sequential predictions
[292]

Gomez, ukasz Kaiser, and Illia Polosukhin

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems , NeurIPs'17, page 6000–6010, Re...

2017
[293]

Neural Machine Translation with Byte - Level Subwords , December 2019

Changhan Wang, Kyunghyun Cho, and Jiatao Gu. Neural Machine Translation with Byte - Level Subwords , December 2019. arXiv:1909.03341

2019 arXiv
[294]

A systematic literature review on the use of deep learning in software engineering research

Cody Watson, Nathan Cooper, David Nader - Palacio, Kevin Moran, and Denys Poshyvanyk. A systematic literature review on the use of deep learning in software engineering research. CoRR , abs/2009.06520, 2020

2009 arXiv
[295]

Executable code actions elicit better llm agents, 2024

Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji. Executable code actions elicit better llm agents, 2024

2024
[296]

Yuxiang Wei, Olivier Duchenne, Jade Copet, Quentin Carbonneaux, Lingming Zhang, Daniel Fried, Gabriel Synnaeve, Rishabh Singh, and Sida I. Wang. Swe-rl: Advancing llm reasoning via reinforcement learning on open software evolution, 2025

2025
[297]

Herbsleb, Alexandra Holloway, and Scott Davidoff

David Gray Widder, Laura Dabbish, James D. Herbsleb, Alexandra Holloway, and Scott Davidoff. Trust in collaborative automation in high stakes software engineering work: A case study at nasa. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems , CHI ...

2021
[298]

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mari...

2020
[299]

Transparency: Motivations and Challenges , page 23–40

Adrian Weller. Transparency: Motivations and Challenges , page 23–40. Springer-Verlag, Berlin, Heidelberg, 2022

2022
[300]

Deep Representations for Software Engineering

M White. Deep Representations for Software Engineering . In Proceedings of the 37th IEEE/ACM International Conference on Software Engineering (ICSE'15) , volume 2 of ICSE '15 , pages 781--783, 5 2015

2015

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Asuncion, Arthur U

Hazeline U. Asuncion, Arthur U. Asuncion, and Richard N. Taylor. Software traceability with topic modeling. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1 , ICSE '10, pages 95--104, 2010

2010

[2] [2]

Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng

Mart\' n Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. Tensorflow: a system for lar...

2016

[3] [3]

Learning to represent programs with graphs

Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. Learning to represent programs with graphs. In International Conference on Learning Representations , 2018

2018

[4] [4]

Information retrieval models for recovering traceability links between code and documentation

Antoniol, Canfora, Casazza, and De Lucia. Information retrieval models for recovering traceability links between code and documentation. In Proceedings of the International Conference on Software Maintenance , ICSE'00, pages 40--49, Oct 2000

2000

[5] [5]

Unified pre-training for program understanding and generation

Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. Unified pre-training for program understanding and generation

[6] [6]

Albrecht, Filippos Christianos, and Lukas Sch\"afer

Stefano V. Albrecht, Filippos Christianos, and Lukas Sch\"afer. Multi-Agent Reinforcement Learning: Foundations and Modern Approaches . MIT Press, 2024

2024

[7] [7]

A literature review of automatic traceability links recovery for software change impact analysis

Thazin Win Win Aung, Huan Huo, and Yulei Sui. A literature review of automatic traceability links recovery for software change impact analysis. 2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC) , pages 14--24, 2020

2020

[8] [8]

The adverse effects of code duplication in machine learning models of code

Miltiadis Allamanis. The adverse effects of code duplication in machine learning models of code. In Onward! OOPLSA 2019 , pages 143--153, 2019

2019