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arxiv: 2604.16404 · v1 · submitted 2026-03-31 · 💻 cs.SE

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On the Use of Commit Messages for Corrective Software Maintenance: A Systematic Mapping Study

Syful Islam , Stefano Zacchiroli
Authors on Pith no claims yet

Pith reviewed 2026-05-13 23:31 UTC · model grok-4.3

classification 💻 cs.SE
keywords commit messagescorrective maintenancesystematic mapping studybug analysisrepository miningnatural language processingsoftware evolutionbug fix identification
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The pith

Commit messages aid bug analysis and fixes but frequently omit details needed to understand code changes.

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

This mapping study reviews 97 papers published from 2004 to May 2025 on the role of commit messages in corrective software maintenance. It finds rising use of these messages, paired with code diffs, for identifying bugs and documenting fixes through repository mining and NLP or machine learning techniques. Developers are the primary stakeholders considered, and messages are shown to carry useful information for tracking software evolution. However, the studies consistently note that messages often lack sufficient detail to convey the intent behind changes. Other maintenance goals such as automated repair or security receive far less attention in the literature.

Core claim

A systematic mapping of 97 primary sources establishes that commit messages support corrective maintenance tasks, especially bug analysis and bug fix identification, because they carry crucial information helpful for stakeholders to understand and improve the code base through the software evolution process, yet they often lack important information and are not enough to convey the intent of code changes to future readers.

What carries the argument

Systematic mapping study of 97 primary sources that catalogs goals, combined artifacts such as commit diffs, methodologies including repository mining and NLP/AI, and stakeholder roles in corrective maintenance.

If this is right

  • Commit messages will remain central to bug-related maintenance tasks when combined with diffs and automated analysis.
  • Developers should prioritize writing more complete messages to reduce future maintenance effort.
  • Research attention should expand beyond bug analysis to areas such as automated program repair.
  • Repository mining paired with NLP and machine learning will continue as the dominant approach for studying these messages.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Tools that automatically suggest or complete commit messages could directly address the documented gaps in information quality.
  • Standard guidelines for commit message content might improve consistency across open-source and industrial projects.
  • Integrating commit message analysis with other artifacts like test results could strengthen corrective maintenance workflows.
  • Training programs for new developers could emphasize the long-term value of detailed change descriptions.

Load-bearing premise

The search strategy and inclusion criteria used to identify the 97 primary sources produced a representative and unbiased sample of all relevant literature published up to May 2025.

What would settle it

Discovery of a large body of additional peer-reviewed studies on commit messages in corrective maintenance that show markedly different usage patterns, methodologies, or quality assessments would contradict the mapped landscape.

Figures

Figures reproduced from arXiv: 2604.16404 by Stefano Zacchiroli, Syful Islam.

Figure 1
Figure 1. Figure 1: Protocol used for the selection of primary sources. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Query template applied on the selected digital li [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Year-wise publication (rolling 2-year) trend. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Publication venue types [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Publication venues. are further discussed in Section 3, providing a comprehensive in￾terpretation of the findings. 3 Results In this section, we review and analyze the information extracted from 97 primary sources following the scales and dimensions iden￾tified for each research question in [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Frequency and co-occurrence of software artifacts used to support corrective software maintenance activities. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Methodology types and their usage patterns. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Stakeholder types and their mention patterns. [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
read the original abstract

Corrective maintenance is crucial to ensure the quality of software, thereby improving reliability and user experience. In a version control system (VCS), developers write commit messages to document their changes and support later maintenance. Still, to this day, no secondary study has mapped the research landscape of how commit messages have been used in corrective software maintenance. We present a systematic mapping study of 97 primary sources published between 2004 and May 2025, where we examine the goals, potential utilization of source code artifacts along with commit messages, methodologies, stakeholders, and the key findings about their influence on corrective maintenance. Our analysis reveals a growing interest in the usage of commit messages to perform corrective maintenance tasks, in particular for bug analysis and bug fix identification goals. Surprisingly few studies address other themes such as automated program repair, security development practices, etc. We find that the software artifacts most used in combination with commit messages are commit "diffs" and that repository mining, together with natural language processing (NLP) and artificial intelligence/machine learning (AI/ML) are the methodological foundations of studies in this field. Among stakeholders considered in previous studies, developers play the most important role in shaping corrective maintenance practices. Key findings in previous studies about commit messages establish their significant role in corrective maintenance, due to the fact that they carry crucial information helpful for stakeholders to understand and improve the code base through the software evolution process. Often, though, commit messages lack important information and are not enough to convey the intent of code changes to future readers.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents a systematic mapping study of 97 primary sources (2004–May 2025) examining the use of commit messages in corrective software maintenance. It synthesizes trends in research goals (primarily bug analysis and bug-fix identification), combined artifacts (especially commit diffs), methodologies (repository mining plus NLP/AI/ML), stakeholders (developers dominant), and key findings that commit messages supply crucial information for understanding code evolution yet frequently lack sufficient detail to convey change intent.

Significance. If the 97-source sample proves representative, the mapping supplies a useful baseline for the field by documenting growth in the topic, identifying under-explored areas such as automated program repair and security practices, and highlighting the methodological reliance on repository mining and machine-learning techniques. Such an overview can usefully orient future empirical work on improving commit-message quality.

major comments (2)
  1. [Methodology] Search and selection process (methodology section): the abstract and high-level description provide no explicit search strings, database list, or inclusion/exclusion criteria, leaving the representativeness of the 97-paper sample unverified and directly undermining the synthesized claims about dominant goals, “surprisingly few studies,” and overall trends.
  2. [Results / Key Findings] Key findings synthesis: the assertion that commit messages “often lack important information” is presented as a central result yet lacks any quantitative breakdown (e.g., fraction of the 97 sources supporting the claim or coding scheme used), making it impossible to gauge the strength or consistency of the evidence.
minor comments (2)
  1. [Abstract] Abstract: the cutoff date “May 2025” appears forward-looking; confirm whether this is a typographical error for 2024 or an intended future date.
  2. [Abstract / Results] Terminology: the phrase “source code artifacts along with commit messages” is used repeatedly without an explicit enumeration of the artifact categories beyond “commit diffs”; a table or bullet list would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments on our systematic mapping study. We address each major comment below and outline the revisions we will make to improve the manuscript.

read point-by-point responses

  1. Referee: [Methodology] Search and selection process (methodology section): the abstract and high-level description provide no explicit search strings, database list, or inclusion/exclusion criteria, leaving the representativeness of the 97-paper sample unverified and directly undermining the synthesized claims about dominant goals, “surprisingly few studies,” and overall trends.

    Authors: We agree that the abstract and high-level description do not explicitly list the search strings, databases, and inclusion/exclusion criteria. These are detailed in the Methodology section of the paper. To enhance accessibility and allow immediate verification of the sample's representativeness, we will revise the abstract and introduction to include a brief overview of the search strategy, the databases queried, and the key inclusion/exclusion criteria. This addition will strengthen the transparency without altering the core methodology. revision: yes

  2. Referee: [Results / Key Findings] Key findings synthesis: the assertion that commit messages “often lack important information” is presented as a central result yet lacks any quantitative breakdown (e.g., fraction of the 97 sources supporting the claim or coding scheme used), making it impossible to gauge the strength or consistency of the evidence.

    Authors: The observation that commit messages often lack important information is synthesized from the key findings reported in the primary studies. While the current version presents this qualitatively, we recognize the value of providing quantitative support. In the revised manuscript, we will include a quantitative breakdown in the Results section, specifying the number or fraction of the 97 sources that contribute to this finding, along with a description of the coding scheme employed to classify the key findings. This will allow readers to better assess the robustness of the claim. revision: yes

Circularity Check

0 steps flagged

No circularity: synthesis from external primary sources

full rationale

This systematic mapping study derives all claims by aggregating and classifying findings across 97 externally identified primary sources. No equations, fitted parameters, predictions, or self-referential derivations appear; the reported trends in goals, artifacts, methodologies, and stakeholder roles are direct syntheses from the reviewed literature. The search and inclusion process is a methodological input rather than a derivation that reduces to the paper's own outputs, and no load-bearing self-citations or uniqueness theorems from the authors' prior work are invoked.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the standard assumption that a systematic literature search yields a representative sample; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption The search strategy and inclusion criteria capture a representative sample of relevant literature
    Standard premise in systematic mapping studies to justify generalizability of synthesized findings.

pith-pipeline@v0.9.0 · 5576 in / 1259 out tokens · 51219 ms · 2026-05-13T23:31:21.336924+00:00 · methodology

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Reference graph

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