Rethinking Training Data for Generating Code Review Comments

Diego Elias Costa; Estefania Pakarati-Cofre; Juan Pablo Sandoval Alcocer; Leonardo Centellas-Claros

REVIEW 3 major objections 6 minor 14 references

Rethinking Training Data for Generating Code Review Comments

T0 review · 3 major / 6 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read The paper argues that nearly half of sampled code-review training pairs misalign with the task of generating actionable feedback from a diff, and that taxonomy-guided prompts barely improve LLM-based filtering.

desk verdict Useful taxonomy and an honest negative result on LLM filtering, but the headline 48% misalignment rate rides on a contestable definition of 'actionability' that the paper never defends. read the letter →

arxiv 2607.25851 v1 pith:TJGK46VH submitted 2026-07-28 cs.SE

Leonardo Centellas-Claros , Estefania Pakarati-Cofre , Juan Pablo Sandoval Alcocer , Diego Elias Costa This is my paper · ORCID

classification cs.SE

keywords codereviewcommentgenerationtrainingdataqualitymisalignedpairsdatasetcleaningLLM-basedfilteringtaxonomyactionability

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 paper tries to show that the standard way of building training data for automated code-review comment generation—pairing a localized code diff with a human review comment—produces a substantial share of instances that do not teach a model to give actionable feedback. In a random sample of 383 pairs from a widely used dataset, human reviewers judged 184 (48%) as misaligned, and the paper groups them into three categories: semantic ambiguity, lack of actionability, and missing context. It then tests whether making these categories explicit in prompts helps a large language model filter out bad pairs, finding only marginal and inconsistent gains. The upshot is that cleaning a noisy dataset is not enough: the task formulation itself needs richer inputs, explicit validity criteria, and evaluation that rewards actionable comments.

What carries the argument

The key objects are the taxonomy of misaligned training pairs—semantic ambiguity, lack of actionability, and context dependence—and the prompt-engineering experiment built on it. The taxonomy operationalizes what 'misaligned' means by classifying why a diff-comment pair fails to supply a reliable learning signal; the experiment (three system prompts, two LLMs, two input representations) tests whether that taxonomy transfers to automatic filtering, thereby exposing the limits of zero-shot prompting for data cleaning.

What would settle it

Re-annotate the 383 sampled pairs with a broader validity criterion that accepts clarifying and conversational comments as valid training signal; if the misalignment share drops below, say, 25%, the paper's central estimate overstates structural misalignment. Alternatively, if a taxonomy-guided filter with few-shot examples reaches F1 above 0.85 on the same sample, the conclusion that taxonomy cues do not help filtering would fail.

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Extended reading notes

Core claim

The central claim is that a large fraction of diff-comment training pairs are not merely noisy but structurally misaligned with the intended learning objective. In a manual review of 383 randomly sampled pairs, 184 (48%) were judged misaligned: either the comment's intent is ambiguous (25), the comment does not request or justify any change (98), or the comment depends on context outside the diff (61). Incorporating this taxonomy into LLM filtering prompts as definitions, criteria, and categories did not substantially improve filtering over simpler prompts: F1 gains were marginal and inconsistent across models and input representations, and the strongest configuration remained around 0.71 fo

Load-bearing premise

A training pair is counted as misaligned only if an actionable comment cannot be inferred from the diff alone; if the intended task includes clarification, coordination, or full-context reasoning, the 48% estimate would shrink.

Editorial extensions

If this is right

Models trained on these datasets are rewarded for producing fluent, review-like text even when an instance does not support actionable feedback grounded in the diff, explaining generic or weakly grounded outputs.
Cleaning datasets by removing noisy instances is necessary but not sufficient; the 48% misalignment estimate implies that significant portions of the data encode non-actionable or context-dependent review behavior.
To support actionable feedback, input representations should include review-thread history, references to non-local code, project conventions, or CI signals, rather than relying on diffs alone.
Evaluation of generated review comments should prioritize actionability and review intent over surface-level textual similarity.
LLM-based data filtering with zero-shot taxonomy prompts leaves large room for improvement; better validity criteria and richer prompting or model configurations are needed.

Reading between the lines

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

If the 48% estimate generalizes, published performance numbers on this dataset likely overstate quality, since many test-time comments also exhibit the same misalignment; evaluating on an aligned-only subset could change rankings.
The taxonomy could be turned into a diagnostic benchmark for agentic review systems: each category suggests a specific capability (e.g., abstain on insufficient context, retrieve previous comments), making the categories directly testable.
The null filtering result may reflect prompt fidelity rather than taxonomy uselessness; few-shot examples per category or fine-tuned classifiers are a natural next test the paper did not run.
A direct implication the paper leaves implicit: training on the aligned subset alone, or adding an 'abstain' option during inference, could avoid forcing models to hallucinate grounded feedback.

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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

Two things to know. First, the paper's real contribution is not the headline number; it's the taxonomy of misaligned diff-comment pairs and the honest negative result that taxonomy-guided prompts don't fix LLM filtering. Second, the 48% figure is real but fragile: it counts clarifying questions as "non-actionable," and the paper doesn't defend that choice. Reclassifying inquiries as valid drops the misalignment rate to about 33% and moves context dependence to the top category. That still supports their main argument — datasets conflate feedback with broader review discourse — but the emphasis changes.

What's actually good: careful annotation protocol (two reviewers, κ=0.758, consensus, sample size formula), a clear taxonomy with three top-level categories that map to review behavior, and a thorough comparison of 12 LLM-filtering configurations. The negative filtering result is plausible: adding taxonomy categories to the prompt produces at best marginal F1 changes, and some GPT-4o-mini configurations essentially collapse to labeling everything noisy. The paper doesn't oversell the LLM results. The replication package is promised. Credit where due: this is a real empirical study, not a position paper.

Where the soft spots are. The load-bearing definitional choice: "actionable" is operationalized as "can be inferred from the diff alone," and the screening instruction asks whether a model would be expected to generate the comment given only the diff. That definition enters before the taxonomy, so a good chunk of what is called "misalignment" (Inquiry, 56 instances) is a property of the chosen task objective, not of the data. The paper itself calls these "legitimate artifacts" in §V. The 48% estimate therefore needs a robustness check against alternative actionability definitions. Second, the LLM filtering comparison reports no significance tests or confidence intervals; several F1 differences are small and inconsistent across datasets, so the "did not substantially improve" conclusion is weaker than the wording implies. Third, the paper never trains models on aligned vs. misaligned pairs, so the claim that misaligned pairs degrade learning is asserted, not shown. Those are addressable in revision, but they matter.

Who this is for: anyone building or cleaning datasets for review-comment generation, and anyone evaluating whether LLM-based filtering works. A serious referee would get value from this; the taxonomy is a useful vocabulary and the negative result is a useful checkpoint. I'd recommend sending to peer review with a request to (a) show sensitivity of the 48% rate to the actionability definition, (b) add error bars or significance tests on the filtering comparisons, and (c) either temper or test the learning-degradation claim.

Referee Report

3 major / 6 minor

Summary. The paper investigates training data for code review comment generation. It manually samples 383 diff-comment pairs from the CodeReviewer dataset and labels each pair as aligned or misaligned, where misalignment means the comment is not actionable review feedback that could be generated from the diff alone. Two annotators achieve κ=0.758; consensus yields 184 misaligned pairs (48%). A card-sorting analysis produces a three-category taxonomy: semantic ambiguity, lack of actionability, and context dependence. The paper then compares three LLM-filtering prompts (a baseline from Liu et al., a criteria-expanded version, and a taxonomy-augmented version) across two LLMs and two input representations, using both the authors' 383-pair sample and Liu et al.'s 270-pair dataset. The taxonomy-augmented prompt does not substantially improve filtering. The authors conclude that dataset cleaning alone is insufficient and call for explicit validity criteria, richer contextual inputs, and evaluation aligned with review intent.

Significance. If the empirical characterization holds, the paper provides a useful reframing: problematic training pairs are not homogeneous noise but reflect systematic misalignment between the task and current diff-comment representations. The taxonomy (semantic ambiguity, lack of actionability, context dependence) is a practical instrument for dataset design. The negative result for taxonomy-guided LLM filtering is an important caution, especially because it is evaluated on an external dataset. The paper is methodologically transparent: it releases a replication package, uses two independent annotators, reports inter-rater reliability, and covers 12 configurations. However, the central 48% estimate is sensitive to a contestable coding of clarification requests as non-actionable, and the filtering comparison lacks statistical inference. These issues need addressing before the magnitude claims can be fully accepted.

major comments (3)

[§III-A3 / Table I] The coding of 'Inquiry' comments (56 of 383) as non-actionable under 'Lack of Actionability' is a normative choice. The paper's own screening definition asks whether a model 'would be expected to generate' a comment from a diff; clarifying questions are legitimate review feedback that often lead to changes. The paper even notes in §V that these are 'legitimate artifacts of real-world code review, including clarification.' If the 56 Inquiry instances are reclassified as aligned, the misalignment rate drops from 184/383 (48%) to 128/383 (33%), and the most frequent source becomes Context Dependence (61) rather than Lack of Actionability (42 remaining). The paper should justify the actionability criterion with respect to the intended task and report a sensitivity analysis under alternative definitions. As written, the headline 48% is not robust to a reasonable alternative interpretation.
[§IV-B / Table II] The claim that taxonomy-augmented prompting does not substantially improve filtering is made on F1 differences without significance tests or confidence intervals. Example: on the Liu et al. dataset with GPT-3.5 and UPNL, SPDC yields Valid F1 0.71 and Noisy F1 0.63, while SPDCT yields 0.69 and 0.60; on the authors' sample, the corresponding comparison is 0.65/0.68 vs. 0.63/0.62. These differences are within sampling variability. Report bootstrapped confidence intervals or a paired test (e.g., McNemar) to support the negative conclusion, or present the results as purely descriptive without the 'did not substantially improve' framing.
[§IV-A5 / Table II] The random baseline is a single random labeling rather than an expected-random classifier. F1 from one random draw is highly variable; e.g., the reported random baseline on the Liu valid class (0.53) is not the expected value under random labeling. Average over multiple permutations, or provide the theoretical expected F1 given class priors, so the 'beyond chance' comparison is properly calibrated.

minor comments (6)

[§III-A4] The reference [14] for the card-sorting methodology appears to be about data quality for unit test generation; please check the citation or supply the appropriate methodology reference.
[Throughout] The label 'our dataset' for the 383-pair annotated sample is misleading; call it 'our annotated sample' since it is not a dataset in the corpus sense.
[Fig. 1] The yellow highlighting used to indicate modifications may not be visible in monochrome printing; add a textual or boxed indicator.
[§III-A3] The screening instruction phrase 'a model would be expected to generate' is ambiguous: expected by whom, and for what kind of model? Clarify the intended normative criterion.
[§IV-B] The statement 'GPT-3.5-turbo consistently outperformed GPT-4o-mini' is too strong: while this holds for Valid-class F1, GPT-4o-mini often achieves higher Noisy-class F1 (e.g., SPDCT 4o-mini on the authors' sample, Noisy F1 0.71 vs. 0.62 for GPT-3.5). Qualify the claim by class or metric.
[Table II] The green-cell notation 'above the random baseline' should be defined in the caption, especially given the single random draw issue.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the empirical claims are annotation summaries and external-benchmark evaluations, not results identical to their inputs by construction.

full rationale

The paper's central empirical claim, that 184/383 (48%) sampled pairs are misaligned (§III-B), is a prevalence count produced by independent human screening under an explicit, normative definition of actionable review feedback (§III-A3), with substantial inter-rater reliability (κ = 0.758). The taxonomy (§III-A4) was constructed after that screening via open card sorting over the already-labeled misaligned pairs; it is a descriptive explanation of why those pairs failed the screening definition, not a device used to derive the 48% figure. The second main claim, that adding taxonomy-derived categories to prompts did not substantially improve filtering (§IV-B), is evaluated on two datasets — including the external Liu et al. dataset — and compared against a random baseline and F1 metrics for both classes. The taxonomy prompt is an independent experimental condition; its categories are not the ground-truth labels, which come from human annotations. There are no fitted parameters renamed as predictions, no load-bearing self-citations (references [6] and [8] are external prior work), and no imported uniqueness theorems. The normative definition of 'actionable' — which places clarifying inquiries under lack of actionability — is a construct-validity or sensitivity concern, not circularity, because the paper does not define the result in terms of itself or fit anything to the target claim.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No fitted numeric parameters or invented entities. The central estimate depends on a set of measurement assumptions: random sampling from one dataset, a validity criterion defined as diff-grounded actionability, and human annotations as ground truth. The taxonomy-guided prompt is one ad hoc operationalization; the negative result is conditional on it.

assumptions (5)

standard math A 95%-confidence, 5%-margin random sample of 383 pairs estimates the dataset-level misalignment proportion.
Used to size the manual sample in §III-A2; standard but assumes random draws and ignores clustering by repository or project.
domain assumption The CodeReviewer dataset [3] is representative of current practice in review-comment training data.
Generalizations from one dataset to 'review comment datasets' (§III-A1) assume representativeness; the paper does not validate on other datasets.
domain assumption A training pair is valid only if it enables generating actionable review feedback from localized (diff-only) input.
The screening step in §III-A3 operationalizes misalignment against this objective; the entire 48% estimate is conditional on this design choice.
domain assumption The two postgraduate annotators' consensus labels are reliable ground truth (κ=0.758).
Subjective judgment about 'actionability' and 'context dependence' is treated as truth; other annotators could shift category boundaries despite substantial agreement.
ad hoc to paper Prompt SP_DCT (Definition+Criteria+Taxonomy) is a fair operationalization of incorporating the taxonomy into LLM filtering.
The negative conclusion about taxonomy-guided filtering is only as strong as this specific prompt; a different prompt could change the result.

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Cite this review

Pith. "Pith review of Rethinking Training Data for Generating Code Review Comments." pith.science (2026). https://pith.science/paper/TJGK46VH

@misc{pith2026260725851,
  author       = {Pith},
  title        = {Pith review of: Rethinking Training Data for Generating Code Review Comments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TJGK46VH}},
  note         = {Machine review of arXiv:2607.25851}
}

read the original abstract

Generating code review comments has become a prominent research direction in automated code review, commonly formulated as a text generation task over diff-comment pairs. Despite advances in learning-based approaches, generated review comments are often generic, weakly grounded, or non-actionable. Recent studies have also shown that review comment datasets contain noisy or unsuitable training instances, motivating LLM-based dataset cleaning approaches. In this paper, we argue that problematic training instances are not homogeneous and that some limitations stem from deeper issues in the task formulation itself. Through an empirical inspection of a widely used review comment dataset, we identify misaligned training pairs: instances where the relationship between the code change and the review comment does not provide a reliable learning signal for generating actionable review feedback from localized inputs. We derive a taxonomy of misalignment capturing three recurring sources: semantic ambiguity, lack of actionability, and context dependence. We further explore whether incorporating this taxonomy into LLM-based filtering improves the identification of problematic training instances, observing that detecting misaligned training pairs remains challenging. Based on these observations, we argue that improving review comment generation requires more than dataset cleaning alone, motivating explicit validity criteria, richer contextual inputs, and evaluation practices aligned with review intent and actionability.

Figures

Figures reproduced from arXiv: 2607.25851 by the authors.

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Reference graph

Works this paper leans on

14 extracted references · 1 canonical work pages

[1]

Towards automating code review activities,

R. Tufano, L. Pascarella, M. Tufano, D. Poshyvanyk, and G. Bavota, “Towards automating code review activities,” inProceedings of the 43rd International Conference on Software Engineering, ser. ICSE ’21. IEEE Press, 2021, p. 163–174. [Online]. Available: https://doi.org/10.1109/ICSE43902.2021.00027

arXiv 2021
[2]

Autotransform: automated code transformation to support modern code review process,

P. Thongtanunam, C. Pornprasit, and C. Tantithamthavorn, “Autotransform: automated code transformation to support modern code review process,” inProceedings of the 44th International Conference on Software Engineering, ser. ICSE ’22. New York, NY , USA: Association for Computing Machinery, 2022, p. 237–248. [Online]. Available: https://doi.org/10.1145/351...

arXiv 2022
[4]

Commentfinder: a simpler, faster, more accurate code review comments recommendation,

Y . Hong, C. Tantithamthavorn, P. Thongtanunam, and A. Aleti, “Commentfinder: a simpler, faster, more accurate code review comments recommendation,” inProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ser. ESEC/FSE 2022. New York, NY , USA: Association for Computing Machine...

arXiv 2022
[5]

Auger: automatically generating review comments with pre-training models,

L. Li, L. Yang, H. Jiang, J. Yan, T. Luo, Z. Hua, G. Liang, and C. Zuo, “Auger: automatically generating review comments with pre-training models,” inProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ser. ESEC/FSE 2022. New York, NY , USA: Association for Computing Machiner...

arXiv 2022
[6]

Code review automation: Strengths and weaknesses of the state of the art,

R. Tufano, O. Dabi ´c, A. Mastropaolo, M. Ciniselli, and G. Bavota, “Code review automation: Strengths and weaknesses of the state of the art,”IEEE Transactions on Software Engineering, vol. 50, no. 2, pp. 338–353, 2024

2024
[7]

Improving automated code reviews: Learning from experience,

H. Y . Lin, P. Thongtanunam, C. Treude, and W. Charoenwet, “Improving automated code reviews: Learning from experience,” in Proceedings of the 21st International Conference on Mining Software Repositories, ser. MSR ’24. New York, NY , USA: Association for Computing Machinery, 2024, p. 278–283. [Online]. Available: https://doi.org/10.1145/3643991.3644910

arXiv 2024
[8]

Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation ,

C. Liu, H. Y . Lin, and P. Thongtanunam, “ Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation ,” in2025 IEEE/ACM 22nd International Conference on Mining Software Repositories (MSR). Los Alamitos, CA, USA: IEEE Computer Society, Apr. 2025, pp. 236–248. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/MSR66628.2025.00043

arXiv 2025
[9]

A systematic literature review and taxonomy of modern code review,

N. Davila and I. Nunes, “A systematic literature review and taxonomy of modern code review,”Journal of Systems and Software, vol. 177, p. 110951, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0164121221000480

2021

Show all 14 references

[10]

Automating code review: A systematic literature review,

R. Tufano and G. Bavota, “Automating code review: A systematic literature review,” 2025. [Online]. Available: https://arxiv.org/abs/2503.09510

2025 arXiv
[11]

Fine-tuning large language models to improve accuracy and comprehensibility of automated code review,

Y . Yu, G. Rong, H. Shen, H. Zhang, D. Shao, M. Wang, Z. Wei, Y . Xu, and J. Wang, “Fine-tuning large language models to improve accuracy and comprehensibility of automated code review,”ACM Trans. Softw. Eng. Methodol., vol. 34, no. 1, Dec. 2024. [Online]. Available: https://d...

2024 doi
[12]

Llama-reviewer: Advancing code review automation with large language models through parameter-efficient fine-tuning,

J. Lu, L. Yu, X. Li, L. Yang, and C. Zuo, “Llama-reviewer: Advancing code review automation with large language models through parameter-efficient fine-tuning,” 2023. [Online]. Available: https://arxiv.org/abs/2308.11148

2023 arXiv
[13]

Exploring the potential of chatgpt in automated code refinement: An empirical study,

Q. Guo, J. Cao, X. Xie, S. Liu, X. Li, B. Chen, and X. Peng, “Exploring the potential of chatgpt in automated code refinement: An empirical study,” inProceedings of the IEEE/ACM 46th International Conference on Software Engineering, ser. ICSE ’24. New York, NY , USA: Associati...

2024
[14]

Less is more: On the importance of data quality for unit test generation,

J. Zhang, X. Hu, S. Gao, X. Xia, D. Lo, and S. Li, “Less is more: On the importance of data quality for unit test generation,”Proc. ACM Softw. Eng., vol. 2, no. FSE, Jun. 2025. [Online]. Available: https://doi.org/10.1145/3715778

2025 doi
[15]

Replication package

“Replication package.” [Online]. Available: https://doi.org/10.6084/m9.figshare.32296611

doi

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.

[1] [1]

Towards automating code review activities,

R. Tufano, L. Pascarella, M. Tufano, D. Poshyvanyk, and G. Bavota, “Towards automating code review activities,” inProceedings of the 43rd International Conference on Software Engineering, ser. ICSE ’21. IEEE Press, 2021, p. 163–174. [Online]. Available: https://doi.org/10.1109/ICSE43902.2021.00027

arXiv 2021

[2] [2]

Autotransform: automated code transformation to support modern code review process,

P. Thongtanunam, C. Pornprasit, and C. Tantithamthavorn, “Autotransform: automated code transformation to support modern code review process,” inProceedings of the 44th International Conference on Software Engineering, ser. ICSE ’22. New York, NY , USA: Association for Computing Machinery, 2022, p. 237–248. [Online]. Available: https://doi.org/10.1145/351...

arXiv 2022

[3] [4]

Commentfinder: a simpler, faster, more accurate code review comments recommendation,

Y . Hong, C. Tantithamthavorn, P. Thongtanunam, and A. Aleti, “Commentfinder: a simpler, faster, more accurate code review comments recommendation,” inProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ser. ESEC/FSE 2022. New York, NY , USA: Association for Computing Machine...

arXiv 2022

[4] [5]

Auger: automatically generating review comments with pre-training models,

L. Li, L. Yang, H. Jiang, J. Yan, T. Luo, Z. Hua, G. Liang, and C. Zuo, “Auger: automatically generating review comments with pre-training models,” inProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ser. ESEC/FSE 2022. New York, NY , USA: Association for Computing Machiner...

arXiv 2022

[5] [6]

Code review automation: Strengths and weaknesses of the state of the art,

R. Tufano, O. Dabi ´c, A. Mastropaolo, M. Ciniselli, and G. Bavota, “Code review automation: Strengths and weaknesses of the state of the art,”IEEE Transactions on Software Engineering, vol. 50, no. 2, pp. 338–353, 2024

2024

[6] [7]

Improving automated code reviews: Learning from experience,

H. Y . Lin, P. Thongtanunam, C. Treude, and W. Charoenwet, “Improving automated code reviews: Learning from experience,” in Proceedings of the 21st International Conference on Mining Software Repositories, ser. MSR ’24. New York, NY , USA: Association for Computing Machinery, 2024, p. 278–283. [Online]. Available: https://doi.org/10.1145/3643991.3644910

arXiv 2024

[7] [8]

Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation ,

C. Liu, H. Y . Lin, and P. Thongtanunam, “ Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation ,” in2025 IEEE/ACM 22nd International Conference on Mining Software Repositories (MSR). Los Alamitos, CA, USA: IEEE Computer Society, Apr. 2025, pp. 236–248. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/MSR66628.2025.00043

arXiv 2025

[8] [9]

A systematic literature review and taxonomy of modern code review,

N. Davila and I. Nunes, “A systematic literature review and taxonomy of modern code review,”Journal of Systems and Software, vol. 177, p. 110951, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0164121221000480

2021