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REVIEW 5 major objections 6 minor 21 references

Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?

T0 review · 5 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Testing CodeGen1-Multi from 350M to 16.1B parameters on bug fixing, code translation, and commit message generation, this paper finds no evidence of emergent capabilities.

desk verdict A careful, honest negative-result study on emergence in SE, but the detector is too weakly validated to fully support the conclusion. read the letter →

arxiv 2411.17927 v1 pith:ZVSCPYGN submitted 2024-11-26 cs.SE

Conor O'Brien , Daniel Rodriguez-Cardenas , Alejandro Velasco , David N. Palacio , Denys Poshyvanyk This is my paper · ORCID
classification cs.SE
keywords emergentcapabilitieslargelanguagemodelssoftwareengineeringmodelscalingbugfixingcodetranslationcommitmessagegenerationEMERGENTEVAL
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 asks whether large language models for software engineering acquire emergent capabilities—abilities that appear suddenly and unpredictably only after a model reaches a certain size—rather than improving smoothly with scale. To answer, the authors build EMERGENTEVAL, a model-agnostic pipeline that grades a model family on a task at several model sizes and measures how far the scores deviate from a linear growth curve. They apply it to the CodeGen1-Multi family at 350M, 2.7B, 6.1B, and 16.1B parameters, covering bug fixing, code translation, and commit message generation. In the unperturbed tasks, all regression values fall below the pre-set threshold $t=0.10$, and the one perturbed case that crosses it represents a performance drop, not a jump; the paper reports no evidence of emergent capabilities. If correct, this means practitioners should not expect unpredictable capability jumps from scaling up these models within the tested range.

What carries the argument

The load-bearing object is EMERGENTEVAL, a pipeline whose inputs are a task $K$, a model family $F$, one or more metrics $M$, a regression method $R$, and a threshold $t$. The regression fits expected scores from the available model scales, and the deviation between observed and predicted scores is compressed into a single regression value $p$ using RMSD or MAE; if $p > t$, the capability is classified as emergent, otherwise as predictable growth. The case studies instantiate this with linear regression, $t = 0.10$ set before testing, bootstrapping at subsample size 50 over 500 iterations, and perturbation-based checks using CheckList and Levenshtein distance, so that 'unexpected jump' becomes an operational, quantitative deviation from linearity.

What would settle it

Run EMERGENTEVAL on CodeGen1-Multi with additional scale points (for example 1B, 3B, 10B, and 20B parameters) on the same three tasks; if any unperturbed task shows a positive performance jump with regression values $p > 0.10$ under both RMSD and MAE, the paper's no-emergence conclusion for that task would be overturned. A second check would be to rerun the perturbed bug-fixing prompt 1 case and see whether the RMSD crossing persists with a larger sample and whether MAE also crosses once the test harness accommodates multi-line output.

Watch

Extended reading notes

Core claim

On its own terms, the central result is negative: for the three selected software-engineering tasks and the prompts tested, CodeGen1-Multi does not show emergent capabilities between 350M and 16.1B parameters. Bug fixing and code translation scores under BLEU and CodeBLEU rise only slightly and roughly linearly with scale, Exact Match stays near zero, and commit message generation scores are near zero at every scale. The pipeline's regression values $p$, computed with RMSD and MAE against a linear fit, are almost always far below the threshold $t = 0.10$; the single crossing, on perturbed bug-fixing inputs under prompt 1, comes from a sharp decline at the largest scale and is read as negative rather than positive emergence. The paper concludes that over the observed scales, scaling produces modest, roughly linear gains at best, and that this pattern is consistent with treating apparent emergence as a phenomenon that depends heavily on the metric, the prompt, and the task.

Load-bearing premise

The whole negative finding rests on one premise: that how far four measured scores fall from a straight line, compared with a cutoff of 0.10 chosen before the test, reliably detects whether a capability has emerged.

Editorial extensions

If this is right

  • For CodeGen1-Multi between 350M and 16.1B parameters, scaling alone does not unlock new task abilities on the three tasks tested; gains are modest and roughly linear.
  • Prompting strategy appears to affect performance more than model scale, and one prompt made the largest model perform worse, so scale-up decisions should be tied to measured gains rather than assumed emergence.
  • EMERGENTEVAL gives a quantitative alternative to visual inspection of scaling curves, and it can flag both positive and negative deviations from expected growth.
  • Within this scale range, compute budgeting for bug fixing, code translation, or commit message generation should not assume an unpredictable capability jump will appear.
  • The negative results suggest that conclusions about emergence drawn from natural-language tasks may not transfer automatically to software engineering tasks.

Reading between the lines

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

  • With only four scale points and a linear baseline, the detector may be too coarse to catch emergence that appears at higher scale or in a narrow size window; the paper itself does not claim to rule out emergence outside the tested configurations.
  • The perturbed bug-fixing case shows the verdict can depend on the deviation formula: RMSD crosses the threshold while MAE does not, so a stricter detector would require both metrics to agree before declaring emergence.
  • If emergence is metric-dependent, as earlier work cited by the paper argues, then this negative result does not automatically transfer to other software-engineering tasks or to model families with denser coverage of the scale axis.
  • A natural next experiment is to run the same pipeline on a model family with many released checkpoints and additional SE tasks, using semantic-equivalence metrics as a complement to n-gram overlap.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper proposes EmergentEval, a model-agnostic pipeline for detecting emergent capabilities in LLMs. The pipeline fits a linear regression to performance scores versus model scale (parameter count), computes residual statistics (RMSD and MAE) as a regression value p, and flags the curve as emergent when p exceeds a pre-set threshold t = 0.10. The authors instantiate the pipeline on the CodeGen1-Multi family (350M, 2.7B, 6.1B, 16.1B) across three software engineering tasks: bug fixing, code translation, and commit message generation, using several prompting strategies and metrics (EM, BLEU, CodeBLEU, B-Moses, B-Norm), including Checklist-based perturbed inputs. Their main empirical claim is that they find no evidence of emergent capabilities in the selected tasks over the tested scale range; performance tends to grow approximately linearly or not at all. The paper also contributes a replication package and positions the framework as a reusable evaluation tool for future emergence studies.

Significance. If the negative result holds up, the paper provides a useful empirical data point for the ongoing debate about emergent abilities, extending it to the software engineering domain. It would reassure practitioners that scaling CodeGen1-Multi within the 0.35B-16.1B range does not produce unpredictable capability jumps, countering the intuition that adding scale alone can unlock qualitatively new behavior. The explicit pipeline and replication package are valuable assets, and the use of continuous metrics plus perturbation analysis is methodologically sensible. However, the significance is conditional on the validity of the detection method, which the paper does not establish; the framework and findings are a promising starting point rather than a conclusive answer.

major comments (5)
  1. [Section IV.A and Section III] The central negative claim is only meaningful if EmergentEval would detect genuine emergence when present. The paper calibrates t = 0.10 on unspecified example data and fits linear regressions through exactly four scale points; it provides no power analysis, no confidence intervals on the RMSD/MAE values, and no validation on known emergent curves (e.g., from Wei et al. or Schaeffer et al.). With n = 4, a single high-leverage point can dominate the fit, and residual-based statistics have low power to distinguish a sharp unpredictable jump from ordinary scatter around a line. The authors should add a positive-control experiment using synthetic or real emergent curves, and a sensitivity analysis over t and over the set of scale points.
  2. [Table 2 and Section V.A] The paper's own results demonstrate that the verdict can flip depending on the residual estimator: for prompt1 on the perturbed bug-fixing task, RMSD exceeds t = 0.10 while MAE does not (0.1037 vs. 0.0890 for BLEU, 0.1145 vs. 0.0952 for CodeBLEU), and the authors explicitly decline to draw a conclusion. This is a concrete example of the classification depending on the hand-set threshold and the chosen formula. The paper should report how results vary across a range of t values, justify the choice of RMSD versus MAE as the primary statistic, or adopt a predefined consistency rule that requires both criteria to exceed the threshold.
  3. [Section IV.D and Table 4] The RQ3 commit message generation results are at the floor: B-Moses is exactly zero at all four scales, B-Norm is near zero (0.0017 RMSD and 0.0015 MAE), and the paper states that bootstrapping was skipped because grades were almost universally zero and that many diffs could not be processed due to model resource limits. A pipeline that reports p = 0 <= t for a model that never performs the task is not discriminating between smooth growth and absence of capability. The RQ3 conclusion should be reframed as 'the models could not execute the task under the tested setup' rather than as evidence of non-emergence.
  4. [Section III and Section VIII] The operationalization of emergence as deviation from a linear fit is largely circular. The paper defines emergence as sharp, unpredictable jumps but measures it as residual magnitude from a linear regression chosen by the authors, without justifying that linear growth is the correct null model for these tasks or that residual magnitude is a valid proxy for 'unpredictability.' The Discussion's observation that performance 'roughly increases linearly with model scale' (Section V.D) is derived from the same linear fits used for detection, making the conclusion partly self-confirmatory. The authors should validate the linear null model against alternative functional forms (e.g., log-linear, step functions) or explicitly characterize the non-linear patterns the detector is designed to catch.
  5. [Section III and Section IV.A] The threshold t = 0.10 is applied identically to all metrics, but the observed score ranges differ drastically: EM is 0 or near 0, BLEU and CodeBLEU are mostly below 0.3, and B-Moses/B-Norm are near 0. An absolute threshold can be simultaneously too lax for high-variance metrics and too strict for low-variance ones. The paper should calibrate t per metric, normalize residuals (e.g., by the metric's observed range or by the residual standard deviation), and report how the conclusions change under such normalization.
minor comments (6)
  1. [Abstract and Section I] The abstract contains the typo 'do not not provide evidence'; the same phrase appears in the introduction and should be corrected to 'do not provide evidence.'
  2. [Section II.A] The phrase 'largerly underexplored' is a typo for 'largely underexplored.'
  3. [Table 3 caption] The caption states that prompt2 under CodeBLEU achieves 'RMSD and MAE > 0.40', but the table values are 0.0442 and 0.0401; the caption also says 'few values occur outside the range [0, 0.03)', which is inconsistent with the prompt2 CodeBLEU values being outside that range. The caption should be corrected to reflect the actual numbers.
  4. [Section V.A] The sentence 'This variation indicates that the models are not overly biased or simply parroting the expected responses' is an unsupported interpretation of the Exact Match scores, which are zero or near zero; the paper should clarify what the EM values actually show.
  5. [Section III] The regression statistic is denoted 'p', which is easily confused with a statistical p-value. Consider renaming it (e.g., 'regression residual measure' or 'deviation statistic') to avoid implying inferential significance, since no confidence intervals or hypothesis tests are computed.
  6. [Section IV.A] The description of the threshold calibration is vague: 'generating example data representing smooth and emergent curves' does not specify the generating process, the number of examples, or the criteria for 'approximately separates.' Provide the full calibration procedure, either in the text or in the replication package, so the threshold is reproducible.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: the negative finding is an operational measurement, not an input-equivalent construction.

full rationale

The paper's central claim ('no evidence of emergent capabilities') is not derived from a fitted parameter renamed as a prediction. The pipeline defines emergence operationally as deviation from a linear fit (Section III) and then measures that deviation with RMSD/MAE on data from CodeGen1-Multi evaluated on CodeXGLUE and CoDiSum; the metric values and residuals in Tables 1-4 are external observations. The detector is falsifiable in-sample: Table 2 reports prompt1 RMSD values (0.1037, 0.1145) exceeding the threshold, so the pipeline can produce positive detections. The threshold t=0.10 was calibrated on synthetic example curves (Section IV.A), which is a sensitivity and validity choice, not a circular reduction; the paper even acknowledges in Section VII that the testing framework may be insufficient to discover scale-intrinsic emergence. The only self-citation is to the first author's unpublished thesis [17], used for bootstrap configuration and for reporting regression values; the paper itself displays the underlying data and p-values, so the thesis citation is procedural rather than the sole support for the conclusion. Concerns about four-point power and floor effects (B-Moses scores of 0) are statistical power and construct-validity threats, not equivalences between inputs and outputs. No step in the derivation chain reduces to its own input by construction.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The framework banks on three non-trivial choices that the reader does not pay for elsewhere: the linear null model for scaling, the hand-set threshold t=0.10, and the assumption that the chosen metrics capture the capabilities of interest. No new entities are postulated. The only free parameter that directly changes the verdict is the threshold.

free parameters (1)
  • Regression threshold t = 0.10 (both RMSD and MAE)
    Set by the authors before testing using generated example curves meant to represent smooth and emergent growth (Section IV.A). The verdict 'emergent vs non-emergent' is a direct comparison of residual errors to this value, so the conclusion depends on this hand-set number.
assumptions (4)
  • domain assumption Performance should grow linearly with model scale under the null (non-emergent) hypothesis
    The authors state 'we expect the model's performance to increase linearly with its scale over the family of models we studied' (Sections I and III). No prior empirical or theoretical justification is given for linear rather than log-linear or power-law scaling.
  • ad hoc to paper The calibrated threshold t=0.10 separates smooth from emergent curves
    The threshold is said to be established before testing, but its value was chosen by inspecting example data that the authors generated (Section IV.A). The paper does not fully specify the generated curves or a statistical justification for 0.10.
  • domain assumption The five metrics (EM, BLEU, CodeBLEU, B-Moses, B-Norm) validly represent task capability
    Used to grade all tasks; the authors acknowledge in Internal validity (Section VII) that continuous metrics measure surface similarity rather than correctness and may miss emergent capabilities.
  • standard math Standard linear regression, RMSD, MAE, and bootstrapping formulas
    Invoked without proof (Sections III and IV.B); these are standard statistical tools.

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Cite this review

Pith. "Pith review of Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?." pith.science (2026). https://pith.science/paper/ZVSCPYGN

@misc{pith2026241117927,
  author       = {Pith},
  title        = {Pith review of: Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZVSCPYGN}},
  note         = {Machine review of arXiv:2411.17927}
}
read the original abstract

The adoption of Large Language Models (LLMs) across multiple contexts has sparked interest in understanding how scaling model size might lead to behavioral changes, as LLMs can exhibit behaviors not observed in their smaller counterparts. Understanding these emergent capabilities is essential for advancing LLM development and improving their interpretability across diverse tasks. However, whether LLMs exhibit true emergence in the context of Software Engineering remains an unexplored topic, as most research has focused on NLP tasks. In this paper, we investigate the emergence of capabilities in the context of SE. We propose a model-agnostic pipeline for evaluating this phenomenon across three SE tasks: bug fixing, code translation, and commit message generation. More precisely, for each task, we present a case study instantiating our pipeline to analyze the emergence of capabilities in CodeGen1-multi across four scales ranging from 350M to 16.1B parameters. Our findings do not not provide evidence to support the idea of emergent capabilities resulting from scaling the model size in the selected set of tasks. We hope our results can pave the way to a more nuanced understanding of emergent capabilities of LLMs within the SE domain, guiding future research to focus on task-specific evaluations and the identification of alternative factors contributing to this phenomenon. Our work underscores the importance of task diversity in examining model behaviors and highlights potential limitations in transferring prior understandings of and approaches to emergence from NLP to Software Engineering.

Figures

Figures reproduced from arXiv: 2411.17927 by the authors.

Figure 1
Figure 1. The EMERGENTEVAL pipeline instantiated with particular test cases, four model scales, and a visualized linear regression evaluation, which produces a regression value p. To determine whether a model exhibits emergence capabil￾ities, we need a set of models that only differ in size or scale. This means emergence is a property of certain model sizes. In theory, new models could be created at larger, smaller, or in-bet… view at source ↗
Figure 2
Figure 2. Prompting strategy used on CodeGen1-multi for the code repair tasks. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Prompting strategy used on CodeGen1-multi for the commit message [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (6 more)
Figure 5
Figure 5. Figure 5: Bootstrapping with S = 50 and N = 500 over the CodeXGLUE Bugs2fix task, assessed by metrics BLEU and CodeBLEU, over the results of prompting CodeGen1-multi. (The Exact Match metric is omitted, as it is 0 across all data points). 0 5 10 15 Parameters (billions) 0.0 0.2 …
Figure 6
Figure 6. Figure 6: Bootstrapping with S = 50 and N = 500 over the CodeXGLUE Bugs2fix Checklist perturbed task, assessed by metrics Exact Match, BLEU, and CodeBLEU, over the results of prompting CodeGen1-multi. V. RESULTS A. RQ1: Bug Fixing [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Scatterplot of the relationship between the Levenshtein distance [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 9
Figure 9. Figure 9: CodeGen1-multi performance on CodeXGLUE Code2code test cases, [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: Bootstrapping with S = 50 and N = 500 over the CodeXGLUE CodeTrans task, assessed by metrics Exact Match, BLEU, and CodeBLEU, over the results of prompting CodeGen1-multi [17]. 0 5 10 15 Parameters (billions) 0.0 0.2 0.4 0.6 0.8 1.0 B-Moses B-Moses vs scale 0 5 10 15 …
Figure 11
Figure 11. Figure 11: CodeGen1-multi performance on test cases from the CoDiSum [PITH_FULL_IMAGE:figures/full_fig_p008_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

21 extracted references · 16 canonical work pages

  1. [1]

    Emergent abilities of large language models,

    J. Wei, Y . Tay et al. , “Emergent abilities of large language models,” 2022

    work page 2022

  2. [2]

    Measuring Data,

    M. Mitchell, A. S. Luccioni et al. , “Measuring Data,” Dec. 2022, arXiv:2212.05129 [cs]. [Online]. Available: http://arxiv.org/abs/2212. 05129

    arXiv 2022

  3. [3]

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

    E. Nijkamp, B. Pang et al. , “Codegen: An open large language model for code with multi-turn program synthesis,” 2023

    work page 2023

  4. [4]

    Codegen2: Lessons for training llms on programming and natural languages,

    E. Nijkamp, H. Hayashi et al., “Codegen2: Lessons for training llms on programming and natural languages,” 2023

    work page 2023

  5. [5]

    Are emergent abilities of large language models a mirage?

    R. Schaeffer, B. Miranda, and S. Koyejo, “Are emergent abilities of large language models a mirage?” 2023

    work page 2023

  6. [6]

    Are emergent abilities in large language models just in-context learning?

    S. Lu, I. Bigoulaeva et al. , “Are emergent abilities in large language models just in-context learning?” 2023. 9

    work page 2023

  7. [7]

    Beyond the imitation game: Quantify- ing and extrapolating the capabilities of language models,

    A. Srivastava, A. Rastogi et al., “Beyond the imitation game: Quantify- ing and extrapolating the capabilities of language models,” 2023

    work page 2023

  8. [8]

    Beyond accuracy: Behavioral testing of nlp models with checklist,

    M. T. Ribeiro, T. Wu et al. , “Beyond accuracy: Behavioral testing of nlp models with checklist,” 2020

    work page 2020

Show all 21 references
  1. [9]

    A Structured Review of the Validity of BLEU,

    E. Reiter, “A Structured Review of the Validity of BLEU,” Computational Linguistics , vol. 44, no. 3, pp. 393–401, 09 2018. [Online]. Available: https://doi.org/10.1162/coli a 00322

    2018 doi

  2. [10]

    ORANGE: a method for evaluating automatic evaluation metrics for machine translation,

    C.-Y . Lin and F. J. Och, “ORANGE: a method for evaluating automatic evaluation metrics for machine translation,” in COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics. Geneva, Switzerland: COLING, aug 23–aug 27 2004, pp. 501–507. [Onlin...

    2004

  3. [11]

    Codebleu: a method for automatic evaluation of code synthesis,

    S. Ren, D. Guo et al., “Codebleu: a method for automatic evaluation of code synthesis,” 2020

    2020

  4. [12]

    Bleu: a method for automatic evaluation of machine translation,

    K. Papineni, S. Roukos et al., “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics , 2002, pp. 311–318

    2002

  5. [13]

    Codexglue: A machine learning benchmark dataset for code understanding and generation,

    S. Lu, D. Guo et al., “Codexglue: A machine learning benchmark dataset for code understanding and generation,” CoRR, vol. abs/2102.04664, 2021

    2021 arXiv

  6. [14]

    Pypi/k4black/codebleu,

    K. Chernyshev, “Pypi/k4black/codebleu,” https://pypi.org/project/ codebleu/0.6.1/, 2024, version 0.6.1. [Online]. Available: https://pypi.org/project/codebleu/0.6.1/

    2024

  7. [15]

    Commit message generation for source code changes,

    S. Xu, Y . Yao et al. , “Commit message generation for source code changes,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 . International Joint Conferences on Artificial Intelligence Organization, 7 2019, pp. 3975–3981...

    2019 doi

  8. [16]

    Using large language models for commit message generation: A preliminary study,

    L. Zhang, J. Zhao et al. , “Using large language models for commit message generation: A preliminary study,” 2024

    2024

  9. [17]

    Emergent capabilities of LLMs for software engineering,

    C. S. O’Brien, “Emergent capabilities of LLMs for software engineering,” Master’s thesis, William & Mary, 2024, unpublished. [Online]. Available: https://www.proquest.com/dissertations-theses/ emergent-capabilities-llms-software-engineering/docview/3106577423/ se-2

    2024

  10. [18]

    On the evaluation of commit message generation models: An experimental study,

    W. Tao, Y . Wang et al. , “On the evaluation of commit message generation models: An experimental study,” 2021

    2021

  11. [19]

    On the dangers of stochastic parrots: Can language models be too big?

    E. M. Bender, T. Gebru et al. , “On the dangers of stochastic parrots: Can language models be too big?” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency , ser. FAccT ’21. New York, NY , USA: Association for Computing Machinery, 2021, p. 6...

    2021 doi

  12. [20]

    Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases,

    W. Guo and A. Caliskan, “Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases,” in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , ser. AIES ’21. ACM, Jul. 2021. [Online]. Available: http:...

    2021

  13. [21]

    Social biases in NLP models as barriers for persons with disabilities,

    B. Hutchinson, V . Prabhakaran et al. , “Social biases in NLP models as barriers for persons with disabilities,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, D. Jurafsky, J. Chai et al. , Eds. Online: Association for Computational...

    2020

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