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arxiv: 2505.01469 · v1 · submitted 2025-05-02 · 💻 cs.SE

Automatic techniques for issue report classification: A systematic mapping study

Muhammad Laiq , Felix Dobslaw This is my paper

Pith reviewed 2026-05-22 17:36 UTC · model grok-4.3

classification 💻 cs.SE
keywords issue report classificationsystematic mapping studymachine learningsoftware maintenancebug triagedeep learninglarge language models
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The pith

Existing studies on automatic issue report classification overlook practitioner involvement and real-world adoption factors.

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

This paper performs a systematic mapping study to survey the use of automatic techniques for classifying software issue reports into categories such as bugs or enhancements. It finds that the 46 identified studies apply machine learning, deep learning, and large language models but evaluate them almost exclusively on open-source archival data. The work highlights that these studies rarely involve practitioners and focus narrowly on prediction accuracy while ignoring other factors like explainability, scalability, and generalizability. A sympathetic reader would care because improved classification could help software teams assign resources more effectively, yet current methods may not transfer well to industrial settings.

Core claim

The literature applies various techniques for classifying issue reports, including traditional machine learning and deep learning-based techniques and more advanced large language models. These studies lack the involvement of practitioners, do not consider other potentially relevant adoption factors beyond prediction accuracy such as the explainability, scalability, and generalizability of the techniques, and mainly rely on archival data from open-source repositories only. Therefore, future research should focus on real industrial evaluations, consider other potentially relevant adoption factors, and actively involve practitioners.

What carries the argument

A systematic mapping study that identified and analyzed 46 studies on automatic techniques for issue report classification.

If this is right

  • Techniques must incorporate explainability features to increase trust and adoption by development teams.
  • Evaluations should expand beyond accuracy to include scalability and performance on proprietary industrial datasets.
  • Future work should prioritize partnerships with practitioners to ensure relevance to real triage workflows.
  • Large language models may require domain-specific fine-tuning for effective issue classification in practice.

Where Pith is reading between the lines

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

  • Teams might prefer building custom internal classifiers trained on their own historical issues rather than adopting published open-source solutions.
  • Integrating issue classification with related tasks like effort estimation or duplicate detection could yield more practical tools.
  • Metrics focused on time saved in triaging or reduction in misassigned issues would better demonstrate value than accuracy alone.

Load-bearing premise

The search and selection process captured a representative sample of all relevant studies on automatic issue report classification without significant omissions.

What would settle it

Discovery of several high-impact studies featuring practitioner involvement and industrial evaluations of issue classifiers that were missed by the mapping would undermine the reported gaps.

read the original abstract

Several studies have evaluated automatic techniques for classifying software issue reports to assist practitioners in effectively assigning relevant resources based on the type of issue. Currently, no comprehensive overview of this area has been published. A comprehensive overview will help identify future research directions and provide an extensive collection of potentially relevant existing solutions. This study aims to provide a comprehensive overview of the use of automatic techniques to classify issue reports. We conducted a systematic mapping study and identified 46 studies on the topic. The study results indicate that the existing literature applies various techniques for classifying issue reports, including traditional machine learning and deep learning-based techniques and more advanced large language models. Furthermore, we observe that these studies (a) lack the involvement of practitioners, (b) do not consider other potentially relevant adoption factors beyond prediction accuracy, such as the explainability, scalability, and generalizability of the techniques, and (c) mainly rely on archival data from open-source repositories only. Therefore, future research should focus on real industrial evaluations, consider other potentially relevant adoption factors, and actively involve practitioners.

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

1 major / 2 minor

Summary. The manuscript reports a systematic mapping study of automatic techniques for classifying software issue reports. It identifies 46 primary studies applying traditional machine learning, deep learning, and large language models, and synthesizes three main observations: limited practitioner involvement, narrow focus on prediction accuracy without considering explainability, scalability or generalizability, and predominant use of open-source archival data. The authors conclude that future work should prioritize industrial evaluations and active practitioner engagement.

Significance. If the sample of 46 studies is representative, the mapping provides a useful consolidation of the literature and correctly flags adoption-relevant gaps that have received insufficient attention. Such overviews can help steer the field toward more practical, industry-aligned research on issue report classification.

major comments (1)
  1. [Methodology] Methodology section: The manuscript states that a systematic mapping study was performed and 46 studies were identified, yet provides no explicit search strings, databases, time bounds, inclusion/exclusion criteria, quality assessment, or snowballing procedure. Because the central claims about field-wide gaps (practitioner involvement, adoption factors, data sources) rest on the representativeness of this sample, the absence of a reproducible protocol leaves the synthesis vulnerable to selection bias and prevents readers from verifying completeness.
minor comments (2)
  1. [Results] Table or figure summarizing the 46 studies by technique category, publication year, and data source would improve readability and allow quick assessment of the distribution of open-source versus industrial data.
  2. [Abstract] The abstract lists the three observations but does not quantify them (e.g., how many of the 46 studies involved practitioners). Adding brief counts or percentages would strengthen the summary.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback on our systematic mapping study. We agree that greater methodological transparency is essential for establishing the representativeness of the 46 primary studies and the validity of our synthesized observations regarding practitioner involvement, adoption factors, and data sources. We address the major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Methodology] Methodology section: The manuscript states that a systematic mapping study was performed and 46 studies were identified, yet provides no explicit search strings, databases, time bounds, inclusion/exclusion criteria, quality assessment, or snowballing procedure. Because the central claims about field-wide gaps (practitioner involvement, adoption factors, data sources) rest on the representativeness of this sample, the absence of a reproducible protocol leaves the synthesis vulnerable to selection bias and prevents readers from verifying completeness.

    Authors: We agree that the current manuscript does not provide sufficient explicit detail on the search and selection protocol, which is necessary to demonstrate reproducibility and mitigate concerns about selection bias. In the revised manuscript we will expand the Methodology section to include the complete search strings, the specific databases and repositories queried, the time bounds, the full inclusion and exclusion criteria, any quality assessment criteria applied, and the snowballing procedure (if used). These additions will allow readers to verify the completeness of the sample and will strengthen the foundation for our observations on the identified gaps in the literature. revision: yes

Circularity Check

0 steps flagged

Literature review aggregates external studies; no self-referential reduction

full rationale

The paper is a systematic mapping study that searches for and analyzes 46 independently published external works on automatic issue report classification. Its central claims (lack of practitioner involvement, focus on accuracy over explainability/scalability, reliance on open-source archival data) are summaries and gap identifications drawn from those external papers. No equations, fitted parameters, self-definitions, or self-citation chains appear in the provided text; the derivation does not reduce any result to the paper's own inputs by construction. The search protocol and selection criteria are methodological choices whose validity is external to the reported findings.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is a secondary study that depends on the completeness of prior primary studies and on standard systematic mapping methodology rather than introducing new fitted parameters or invented entities.

axioms (1)
  • domain assumption Standard systematic mapping study guidelines (e.g., search strategy, inclusion criteria, data extraction) produce a representative overview of the field.
    Invoked implicitly when the authors conclude that the 46 studies reflect the state of the literature and its gaps.

pith-pipeline@v0.9.0 · 5707 in / 1202 out tokens · 37175 ms · 2026-05-22T17:36:31.494618+00:00 · methodology

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