Pith. sign in

REVIEW 5 major objections 6 minor 22 references

Towards Leveraging Large Language Model Summaries for Topic Modeling in Source Code

T0 review · 5 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read LLM-generated summaries of stripped-down code can serve as the input for topic modeling, producing meaningful topics without comments or meaningful function names.

desk verdict The summary+BERtopic pipeline is a reasonable new combination, but the 'solely code structure' claim is undercut because the preprocessing only replaced function names, leaving body identifiers fully intact. read the letter →

arxiv 2504.17426 v1 pith:7PW5XVSF submitted 2025-04-24 cs.SE cs.AI

Michele Carissimi , Martina Saletta , Claudio Ferretti This is my paper · ORCID
classification cs.SEcs.AI
keywords sourcecodeanalysistopicmodelingLLMsummarizationcomprehensiondocstringalignmentstructuresemanticssoftwaremaintenancefunctionnameobfuscation
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

The paper proposes that topic modeling for source code can be driven by LLM-generated summaries of code rather than by natural-language clues inside the code. It strips comments, replaces function names with placeholders, asks a language model to describe each function's purpose, and runs topic modeling on those descriptions. The comparison against topics derived from docstrings shows that summary-based topics are close to docstring-based reference topics, and clearly closer than topics derived from function names alone. If this holds, code repositories without documentation can still be organized and searched by topic using only code structure.

What carries the argument

The load-bearing mechanism is a three-stage pipeline. First, preprocessing strips comments and replaces each function name with a placeholder, so the language model sees only code structure. Second, an instruction-tuned language model generates a plain-text purpose description for each function. Third, a transformer-embedding topic modeling method clusters these descriptions into topics, and each function gets a probability distribution over topics. The validation machinery is comparative: an identical topic model is built from the original docstrings as the golden reference, and four distance metrics, mean squared error over topic distributions, overlap of top-10 topics, cosine similarity of top-10 topic vectors, and shared top-5 words, measure how close summary-based and name-based inferences come to that reference.

What would settle it

Re-run the comparison using human expert labels instead of docstrings as the reference on a new set of Python functions; if summary-based topics do not beat function-name topics at predicting expert labels, the central claim fails.

Watch

Extended reading notes

Core claim

The central claim is that the structure of source code alone, with comments removed and identifiers obfuscated, carries enough semantics for an LLM to write descriptions from which a topic model can recover coherent, interpretable topics. The paper reports an average topic coherence near 0.60 for summary-based topics, versus 0.38 for topics built from original docstrings, and reports that summary-based topic assignments are more similar to docstring-based reference topics than are assignments made from function names on every metric where the comparison is defined. The conclusion is that LLM summaries compensate for missing documentation and outperform the natural semantics encoded in function identifiers.

Load-bearing premise

The evaluation assumes docstrings are the correct ground truth for what a code topic should be; if many docstrings are stale, generic, or mismatched to the code, the reference targets that everything is compared against are unreliable.

Editorial extensions

If this is right

  • Topic models can be built for code with no comments, no docstrings, and no meaningful identifiers, making undocumented legacy or third-party code accessible to topic-based organization.
  • Summary-based topic assignment can serve as a proxy when documentation exists but is incomplete, with performance close to using the docstrings themselves.
  • Function names alone are a weaker signal than whole-code summaries, which implies identifier naturalness should not be the primary target for code topic extraction.
  • High topic coherence from summaries suggests LLM-generated descriptions can be a reusable substrate for software maintenance tasks such as automatic tagging, documentation, and repository reorganization.

Reading between the lines

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

  • If the summaries are what carry the semantics, the pipeline should transfer to other languages and other code granularities such as classes or modules with only prompt changes; a direct test would be to rerun it on Java or C++ snippets.
  • The docstring golden standard anchors the evaluation to one human-written documentation style; a stricter test would compare against task performance, such as code search retrieval or bug localization, where topic quality shows up behaviorally.
  • The coherence gap between summaries and docstrings may partly reflect that LLM summaries are more uniform in vocabulary than docstrings; controlling for summary length or vocabulary would test whether the gain is semantic or stylistic.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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 a topic-modeling pipeline for Python source code that replaces function names with a placeholder, removes comments, asks an LLM (Gemma2 2B-it) to generate natural-language summaries of the obfuscated code, and then applies BERTopic to those summaries. The authors compare the resulting topics, via four distance metrics, against topics inferred from docstrings and from tokenized function names, reporting that summary-based topics align more closely with docstring-based reference topics than do function-name-based topics, and that the summary-based topics have higher internal coherence. The stated central claim is that this approach models topics from source code by relying solely on code structure rather than natural-language elements. The paper includes public data and code on Zenodo.

Significance. If the central claim held, the paper would offer a practical method for topic modeling on poorly documented or identifier-poor code, and it would extend the line of work on LLM-based code understanding. The main strengths are the simple, reproducible pipeline, the public release of data and code, and the use of multiple complementary evaluation metrics. However, the strongest conceptual claim (that the method relies solely on code structure) is not yet supported because the obfuscation step leaves most identifiers inside function bodies intact, and the evaluation lacks statistical significance testing. The paper is best viewed as a promising preliminary study whose headline conclusion requires substantially stronger evidence.

major comments (5)
  1. [Sections 3 and 5] The preprocessing described in Section 3 only replaces 'function names in their declarations' and removes comments; it does not rename local variables, parameters, attributes, class names, or other identifiers appearing inside function bodies. CodeSearchNet Python functions are identifier-rich, so the LLM summaries can be driven by surviving names such as 'response', 'request', or 'dataframe'. Consequently, the claim in Section 5 that the method models topics 'by relying solely on code structure rather than leveraging natural-language elements' is not supported by the experiment as described. The authors should either obfuscate all identifiers (or otherwise control for body-level naturalness) and rerun the experiments, or substantially qualify the claim.
  2. [Section 4, Table 2] Table 2 reports average distances without any variance estimates, confidence intervals, or significance tests. For instance, the d_TOP values 3.46 vs 3.11 and the d_cap values 3.71 vs 2.18 are presented as evidence that summaries outperform function names, but with only 500 evaluation instances it is unclear whether these differences are statistically reliable. A paired test (e.g., Wilcoxon signed-rank or bootstrap) over the per-document distances is straightforward and should be added before the word 'significantly' is used.
  3. [Section 3] The number of topics, nr_topics = 40, was selected after 'running the model multiple times with varying values and evaluating the results' on what appears to be the same corpus used for the reported evaluation. This post-hoc selection on the evaluation data can inflate coherence scores and makes the reported absolute values hard to interpret. The authors should report sensitivity to nr_topics (e.g., a range of values) and clarify whether the held-out 500-instance evaluation set was used at all during parameter selection.
  4. [Section 4] The coherence comparison between summary-based topics (average C_v ≈ 0.60) and docstring-based topics (0.38) is not a fair comparison, because C_v is sensitive to corpus size, vocabulary, and document length, and the two corpora differ on all of these dimensions. The docstring corpus and the summary corpus are different document sets with different vocabularies, so the difference in coherence may reflect corpus properties rather than the quality of the underlying topic representations. A matched comparison, or at least an explicit discussion of this confound, is needed.
  5. [Section 2, Table 2] Treating docstrings as the 'golden standard' for code topics is questionable, since docstrings can be noisy, outdated, or only loosely related to the actual code behavior. Additionally, the only metric that can be computed between Msumm and Mdoc is d_cap, which counts shared top words across different topic vocabularies; this is a weak basis for the claim that the two topic models are 'close.' The authors should discuss the limitations of this reference standard and of the cross-model metric, and ideally validate against an external human-judged topic assignment.
minor comments (6)
  1. [Section 2] The definitions of the four metrics are muddled: the text defines d_TOP three times and never gives the explicit formula for d_cap. The third definition should be labeled d_cap, and the formula 'number of common words between the 5 most relevant words' should be written consistently with the notation introduced earlier.
  2. [Table 1] There are typos in the top-word lists: 'respnse' should be 'response' and 'rended' should likely be 'rendered'. Also, some tokens such as 'colormathcolorconversions' and 'colormathcolorobjects' appear as single vocabulary items, which reduces interpretability; consider whether post-processing should split or filter such tokens.
  3. [Section 3] The description of the prompt template is unclear: the 'Base query' and 'Prompt template' lines suggest concatenation but the exact ordering and the role of the '##### Description:' marker are not fully specified. Providing the exact prompt string used would improve reproducibility.
  4. [Section 3] The paper does not report the decoding parameters for Gemma2 2B-it (e.g., temperature, top-p, max new tokens). Since the summaries are the sole input to the topic model, these parameters can affect the results and should be stated.
  5. [Section 1 and Section 5] There is an internal inconsistency in describing the obfuscation: Section 1 and Section 5 say 'functions’ identifiers' are obfuscated, while Section 3 says only 'function names in their declarations' are replaced. This inconsistency should be resolved, especially in light of the major comment about residual identifiers.
  6. [Figures 2 and 3] Figures 2 and 3 have minimal captions and no axis labels. Figure 3's color-based topic assignments are hard to interpret without a legend or topic-number labels; consider adding these.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: summaries are benchmarked against external docstring-derived topics; residual-identifier confound is a validity issue, not a constructional reduction.

full rationale

The paper's derivation chain is self-contained. Topic models are built from LLM summaries of preprocessed code and from docstrings; the docstring-based topics serve as an external reference target, not as an output of the summary pipeline. The distance metrics (d_MSE, d_TOP, d_TOP_w, d_cap) are defined directly on topic distributions and topic-term matrices without fitting any parameter to the reference. The only tuned quantity, nr_topics=40, is selected by coherence and interpretability on the modeling corpus; this is standard model selection, not a fitted input disguised as a prediction. The one self-citation, [8] (Ferretti and Saletta), is used only to contextualize why identifier-based topics underperform and is not load-bearing for the central comparison. The claim that topics are modeled 'by relying solely on code structure' is overstated because Section 3 says only function names in declarations were replaced and comments removed, leaving body identifiers intact; however, this is an experimental confound that weakens the interpretation, not a step where an output is equivalent to an input by definition. No equation in the paper reduces to another by construction, and no fitted parameter is renamed as a prediction. Therefore, no significant circularity is present.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The evaluation's validity rests on treating docstrings as ground truth and on the C_v coherence measure as a reliable indicator of topic quality for technical text. These are unstated domain assumptions by the authors; the only explicit hand-tuned parameter is the topic count.

free parameters (1)
  • nr_topics = 40
    The number of topics was set to 40 after multiple runs with varying values, balancing coherence score and interpretability (Section 3). This is a data-dependent choice that affects the reported topic quality and the comparison metrics.
assumptions (3)
  • domain assumption Docstrings are a reliable golden standard for code topic membership
    The entire evaluation compares LLM-summary topics to docstring-derived topics; if docstrings are noisy or unrepresentative, the alignment scores lose meaning (Section 2).
  • domain assumption The C_v coherence metric is a valid measure of topic interpretability for code-derived text
    The paper uses average C_v to claim interpretability, but the metric was designed for natural-language corpora and may not transfer to technical code summaries (Section 4).
  • domain assumption The 10,000 sampled functions are representative of Python code
    Results are derived from a random sample of CodeSearchNet; generalizability to other codebases is assumed (Section 3).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Towards Leveraging Large Language Model Summaries for Topic Modeling in Source Code." pith.science (2026). https://pith.science/paper/7PW5XVSF

@misc{pith2026250417426,
  author       = {Pith},
  title        = {Pith review of: Towards Leveraging Large Language Model Summaries for Topic Modeling in Source Code},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7PW5XVSF}},
  note         = {Machine review of arXiv:2504.17426}
}
read the original abstract

Understanding source code is a topic of great interest in the software engineering community, since it can help programmers in various tasks such as software maintenance and reuse. Recent advances in large language models (LLMs) have demonstrated remarkable program comprehension capabilities, while transformer-based topic modeling techniques offer effective ways to extract semantic information from text. This paper proposes and explores a novel approach that combines these strengths to automatically identify meaningful topics in a corpus of Python programs. Our method consists in applying topic modeling on the descriptions obtained by asking an LLM to summarize the code. To assess the internal consistency of the extracted topics, we compare them against topics inferred from function names alone, and those derived from existing docstrings. Experimental results suggest that leveraging LLM-generated summaries provides interpretable and semantically rich representation of code structure. The promising results suggest that our approach can be fruitfully applied in various software engineering tasks such as automatic documentation and tagging, code search, software reorganization and knowledge discovery in large repositories.

Figures

Figures reproduced from arXiv: 2504.17426 by the authors.

Figure 1
Figure 1. Study overview. Source code summaries generated by an LLM are used to identify the latent topics. This topic model [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Coherence scores Documents and their associated topics can also be visual￾ized in a two-dimensional space ( [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Document embeddings visualization. Each point represents a code instance, with colors indicating the assigned topics. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

22 extracted references · 17 canonical work pages

  1. [1]

    Barr, Premkumar T

    Miltiadis Allamanis, Earl T. Barr, Premkumar T. Devanbu, and Charles Sutton. 2018. A Survey of Machine Learning for Big Code and Naturalness. ACM Comput. Surv.51, 4 (2018), 81:1–81:37

    work page 2018

  2. [2]

    Ted J Biggerstaff, Bharat G Mitbander, and Dallas Webster. 1993. The concept assignment problem in program understanding. In

    work page 1993

  3. [3]

    O’Reilly Media, Inc

    Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural language processing with Python: analyzing text with the natural language toolkit. " O’Reilly Media, Inc.". Towards Leveraging Large Language Model Summaries for Topic Modeling in Source Code EASE 2025, 17–20 June, 2025, Istanbul, Türkiye

    work page 2009

  4. [4]

    Blei, Andrew Y

    David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet Allocation. J. Mach. Learn. Res.3 (2003), 993–1022

    work page 2003

  5. [5]

    Rob Churchill and Lisa Singh. 2022. The evolution of topic modeling. Comput. Surveys54, 10s (2022), 1–35

    work page 2022

  6. [6]

    (Gemma Team)

    Morgane Riviere et al. (Gemma Team). 2024. Gemma 2: Improving Open Language Models at a Practical Size.arXiv abs/2408.00118 (2024)

    arXiv 2024

  7. [7]

    (Gemma Team)

    Thomas Mesnard et al. (Gemma Team). 2024. Gemma: Open Models Based on Gemini Research and Technology. arXiv abs/2403.08295 (2024)

    arXiv 2024

  8. [8]

    Claudio Ferretti and Martina Saletta. 2023. Naturalness in Source Code Summarization. How Significant is it?. In31st IEEE/ACM International Conference on Program Comprehension, ICPC. IEEE, 125–134

    work page 2023

Show all 22 references
  1. [9]

    Maarten Grootendorst. 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv abs/2203.05794 (2022). https://arxiv.org/abs/2203.05794

    2022 arXiv

  2. [10]

    Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019. CodeSearchNet Challenge: Evalu- ating the State of Semantic Code Search.arXiv abs/1909.09436 (2019)

    2019 arXiv

  3. [11]

    Martina Iammarino, Lerina Aversano, Mario Luca Bernardi, and Marta Cimitile. 2020. A topic modeling approach to evaluate the comments consistency to source code. In2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–8

    2020

  4. [12]

    Adrian Kuhn, Stéphane Ducasse, and Tudor Gîrba. 2007. Semantic clustering: Identifying topics in source code.Information and software technology49, 3 (2007), 230–243

    2007

  5. [13]

    Anas Mahmoud and Gary Bradshaw. 2017. Semantic topic models for source code analysis. Empirical Software Engineering22 (2017), 1965–2000

    2017

  6. [14]

    Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. 2018. UMAP: Uniform Manifold Approximation and Projection. J. Open Source Softw.3, 29 (2018), 861

    2018

  7. [15]

    Hellendoorn, Bogdan Vasilescu, and Brad A

    Daye Nam, Andrew Macvean, Vincent J. Hellendoorn, Bogdan Vasilescu, and Brad A. Myers. 2024. Using an LLM to Help With Code Understanding. InProceedings of the 46th IEEE/ACM In- ternational Conference on Software Engineering (ICSE). ACM, 97:1–97:13

    2024

  8. [16]

    Arik Reuter, Anton Thielmann, Christoph Weisser, Benjamin Säfken, and Thomas Kneib. 2025. Probabilistic Topic Model- ing With Transformer Representations.IEEE Transactions on Neural Networks and Learning Systems(2025)

    2025

  9. [17]

    Michael Röder, Andreas Both, and Alexander Hinneburg. 2015. Exploring the Space of Topic Coherence Measures. InProceedings of the Eighth ACM International Conference on Web Search and Data Mining, WSDM 2015, Shanghai, China, February 2-6, 2015, Xueqi Cheng, Hang Li, Evgeniy Ga...

    2015

  10. [18]

    Amir M Saeidi, Jurriaan Hage, Ravi Khadka, and Slinger Jansen

  11. [19]

    Shaheen Syed and Marco R. Spruit. 2017. Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Alloca- tion. In2017 IEEE International Conference on Data Science and Advanced Analytics, DSAA. IEEE, 165–174

    2017

  12. [20]

    Ike Vayansky and Sathish AP Kumar. 2020. A review of topic modeling methods. Information Systems94 (2020), 101582

    2020

  13. [1993]

    IEEE, 27–43

    Proceedings Working Conference on Reverse Engineering. IEEE, 27–43

  14. [2015]

    In2015 IEEE 23rd International Conference on Program Comprehension

    ITMViz: Interactive topic modeling for source code analy- sis. In2015 IEEE 23rd International Conference on Program Comprehension. IEEE, 295–298

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.