arxiv: 2604.26118 · v2 · submitted 2026-04-28 · 💻 cs.SE

LLM-Guided Issue Generation from Uncovered Code Segments

Diany Pressato , Honghao Tan , Mariam Elmoazen , Shin Hwei Tan This is my paper

Pith reviewed 2026-05-08 03:04 UTC · model grok-4.3

classification 💻 cs.SE

keywords issue generationLLMcode coveragebug detectionsoftware testingPythonactionable reportsdefect identification

0 comments

The pith

IssueSpecter uses LLMs on uncovered code to generate prioritized reports with 84.6 percent validity in top results.

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

The paper presents IssueSpecter as a tool that locates potential bugs in code segments left untested by existing suites. It pairs standard coverage analysis with an LLM that examines those segments to identify defects and then assembles complete reports containing severity ratings, reproduction steps, and candidate fixes. The method was run on thirteen actively maintained Python projects and produced more than ten thousand reports. Manual review of the highest-ranked reports found that the large majority describe genuine problems or items worth further checks, and the LLM ranking step improves selection quality over simpler rules. The resulting reports are structured so developers can act on them directly rather than having to interpret raw test output.

Core claim

IssueSpecter combines coverage analysis with LLM-based defect identification to produce structured, prioritized issue reports from uncovered code segments. On thirteen Python projects it generated 10,467 reports. Manual annotation of the top 130 ranked issues showed 84.6 percent validity or need for investigation, with only 15.4 percent false positives. LLM-based ranking outperformed rule-based ranking by 50 percent at P@3 and 41 percent in MRR. The approach also achieved an 81.0 percent bug validity rate compared with 76.2 percent for the prior coverage-driven tool CoverUp while supplying immediately usable reproduction steps and fixes.

What carries the argument

Coverage analysis to isolate uncovered segments, followed by LLM-based defect identification and ranking that assembles severity, reproduction steps, and fixes into structured reports.

If this is right

Developers receive reports that already contain reproduction steps and candidate fixes instead of having to interpret test intent.
LLM ranking selects higher-value issues than rule-based ordering, reducing the number of low-value items a developer must review.
The generated reports cover logic errors, boundary conditions, security issues, and state-consistency problems.
Case studies show that real bugs can be reproduced directly from the structured reports without additional manual work.

Where Pith is reading between the lines

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

The same coverage-plus-LLM pattern could be applied to other languages if the underlying model handles their syntax and semantics at comparable accuracy.
Integration into continuous-integration pipelines could surface prioritized issues automatically when new code is pushed.
The candidate fixes supplied in reports could serve as starting points for patches, shortening the time from discovery to resolution.

Load-bearing premise

Human annotators can judge whether an LLM-generated issue report is valid or worth investigation without knowing the original developer intent or seeing runtime behavior.

What would settle it

A study in which the suggested fixes from the generated reports are applied to the projects and the defects are confirmed or refuted by passing and failing tests or by maintainer acceptance.

Figures

Figures reproduced from arXiv: 2604.26118 by Diany Pressato, Honghao Tan, Mariam Elmoazen, Shin Hwei Tan.

**Figure 1.** Figure 1: IssueSpecter’s overall workflow. 2 view at source ↗

**Figure 2.** Figure 2: Prompt template used for automated bug identifi view at source ↗

**Figure 3.** Figure 3: Prompt template used for LLM-based Ranking. view at source ↗

**Figure 5.** Figure 5: Diff of the original and fixed BufferedPrettyStream.iter_body(). a byte array without any size validation, causing out-of-memory conditions when processing large responses. Manifestation. We reproduced the defect by crafting a synthetic 20 MiB response (2,000 chunks of 10 KB each) exceeding the proposed 10 MiB limit, verifying that a ValueError is raised when the limit is exceeded. The generated issue pro… view at source ↗

**Figure 4.** Figure 4: shows the overlap between actionable findings from both tools. For valid bugs, IssueSpecter exclusively identifies 64 bugs while CoverUp exclusively identifies 50, with only 6 bugs found by both tools. For issues requiring further investigation, IssueSpecter exclusively surfaces 51 cases and CoverUp 57, with 15 shared. The low intersection in both categories (6 and 15 respectively) suggests that the two … view at source ↗

**Figure 7.** Figure 7: Diff of the original and fixed read_user_choice() in Cookiecutter. reproduction steps showing exactly how to construct a prompts mapping with string-keyed representations of dict options, making it straightforward to trigger and confirm the crash with minimal manual effort. Root Cause. The handler implicitly assumes all option values are hashable, violating Cookiecutter’s own support for arbitrary JSON str… view at source ↗

read the original abstract

Developers are increasingly overwhelmed by AI-generated issue reports that lack actionability and reproducibility, eroding trust in automated bug detection tools. In this paper, we present IssueSpecter, an automated tool that finds bugs in uncovered code segments and automatically generates prioritized, actionable issue reports. IssueSpecter combines coverage analysis with LLM-based defect identification, producing structured reports complete with severity ratings, reproduction steps, and suggested fixes. We evaluate IssueSpecter on 13 actively maintained Python projects, generating 10,467 issue reports. Manual annotation of the top-130 ranked issues by IssueSpecter confirms that 84.6% of the LLM-generated issues are valid or warrant further investigation, with only 15.4% false positives. LLM-based ranking outperforms rule-based ranking by 50% at P@3 and 41% in MRR. The identified bugs cover a wide variety of types, from logic and boundary errors to security vulnerabilities and state consistency bugs. By ranking issues by priority, IssueSpecter aims to help developers focus their attention on the most impactful bugs first. Finally, we validate IssueSpecter through case studies reproducing real bugs surfaced from its generated issue reports, demonstrating its practical value for automatic bug discovery in open-source Python projects. Compared against CoverUp, a state-of-the-art coverage-driven test generation tool, IssueSpecter achieves a higher bug validity rate (81.0% vs. 76.2%) under identical evaluation conditions, using the same model and the same number of evaluated artifacts per project, while additionally providing structured issue reports with reproduction steps and candidate fixes that are immediately actionable without requiring developers to interpret generated test intent.

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.

Desk Editor's Note private letter to a colleague

IssueSpecter pairs coverage-guided segment selection with LLM defect spotting to produce structured issue reports, but its key validity and ranking numbers rest on human labels whose process is not described.

read the letter

The core idea is straightforward: run coverage to find uncovered code, feed those segments to an LLM to flag defects, then have the model output ranked reports that include severity, reproduction steps, and candidate fixes. They ran it across 13 Python projects, produced over ten thousand reports, and included case studies where the generated issues led to actual reproduced bugs. That combination of coverage targeting plus structured LLM output is the concrete new piece relative to prior test-generation work like CoverUp. The direct head-to-head under matched conditions and the added actionability in the reports are the parts that feel useful on a practical level. The evaluation numbers all trace back to the same manual review of the top 130 ranked issues. The 84.6 percent valid-or-warrant-investigation rate, the 50 percent P@3 and 41 percent MRR gains over rule-based ranking, and the 81 percent versus 76.2 percent edge over CoverUp depend on those labels. The abstract gives no annotation guidelines, no count or background on the annotators, no blinding statement, no agreement statistic, and no operational definition of validity that could be applied without knowing developer intent. That is a real gap; any consistent bias in how the labels were assigned would affect both the absolute claim and the comparative ranking claim. If the full paper supplies a clear methods section with those details, the evidence strengthens. As it stands in the abstract, the numbers look less solid than they first appear. This is for software-engineering researchers and tool developers working on LLM-assisted bug finding or automated issue generation. Someone already building or evaluating such systems would get value from the pipeline and the multi-project results. It is worth sending to peer review because it has a working implementation, real-project scale, and a comparison, even though the labeling section will need close scrutiny.

Referee Report

2 major / 2 minor

Summary. The paper introduces IssueSpecter, a tool combining coverage analysis with LLMs to detect defects in uncovered code segments of Python projects and generate structured, prioritized issue reports that include severity ratings, reproduction steps, and suggested fixes. Evaluated on 13 actively maintained Python projects, it produces 10,467 reports. Manual annotation of the top-130 ranked issues finds 84.6% valid or warranting further investigation (15.4% false positives). LLM-based ranking outperforms rule-based ranking by 50% at P@3 and 41% in MRR. IssueSpecter achieves 81.0% bug validity versus 76.2% for CoverUp under matched conditions, with case studies showing reproduction of real bugs from the generated reports.

Significance. If the manual validation proves reliable, this work could meaningfully advance automated bug detection by focusing on uncovered code and delivering immediately actionable reports rather than raw tests or alerts. The scale (13 projects, >10k reports) and head-to-head comparison with CoverUp provide a concrete baseline for future tools in software engineering.

major comments (2)

[Evaluation] Abstract and Evaluation section: The headline validity rate of 84.6% (and the 81.0% vs. 76.2% comparison to CoverUp) rests entirely on manual labels of the top-130 issues. The manuscript supplies no annotation protocol, number or expertise of annotators, blinding procedure, inter-rater agreement statistic, or operational definition of “valid or warrants further investigation” that can be applied without developer intent or runtime traces. Because the ranking metrics (P@3, MRR) are also scored against these same labels, any systematic bias or low consistency directly undermines both the absolute and comparative claims.
[Evaluation] Evaluation section (comparison paragraph): The statement that IssueSpecter and CoverUp were run “under identical evaluation conditions, using the same model and the same number of evaluated artifacts per project” is load-bearing for the 81.0% vs. 76.2% claim, yet the text does not confirm that the identical set of uncovered segments was used or that bug-validity labeling followed the same criteria for both tools.

minor comments (2)

The 13 projects are described only as “actively maintained Python projects”; listing their names, versions, and repository links (or a replication package) would improve reproducibility.
The abstract and results text use “bug validity rate” and “valid or warrant further investigation” interchangeably; a single consistent term and a short footnote defining it would reduce ambiguity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback and for acknowledging the potential significance of IssueSpecter in advancing automated bug detection through coverage-guided LLM analysis. We agree that greater transparency in the evaluation methodology is essential to support the reported validity rates and comparisons. We address each major comment below and commit to revisions that will strengthen the manuscript.

read point-by-point responses

Referee: [Evaluation] Abstract and Evaluation section: The headline validity rate of 84.6% (and the 81.0% vs. 76.2% comparison to CoverUp) rests entirely on manual labels of the top-130 issues. The manuscript supplies no annotation protocol, number or expertise of annotators, blinding procedure, inter-rater agreement statistic, or operational definition of “valid or warrants further investigation” that can be applied without developer intent or runtime traces. Because the ranking metrics (P@3, MRR) are also scored against these same labels, any systematic bias or low consistency directly undermines both the absolute and comparative claims.

Authors: We acknowledge that the current manuscript does not provide a detailed annotation protocol, which is a valid concern for assessing the reliability of the 84.6% validity rate, the 15.4% false positive rate, and the ranking metrics (P@3 and MRR). In the revised version, we will add a dedicated subsection titled 'Manual Annotation Protocol' in the Evaluation section. This subsection will specify: (1) the operational definition of 'valid or warrants further investigation' as an issue report that, based on static inspection of the uncovered code segment and the generated report, identifies a plausible defect (such as logic errors, boundary conditions, security issues, or state inconsistencies) that merits developer attention or further investigation; (2) the number and expertise of annotators (two authors with over 15 years combined experience in Python software engineering); (3) the annotation process, including independent labeling followed by discussion to resolve disagreements; (4) inter-rater agreement measured via Cohen's kappa; and (5) confirmation that annotators were blinded to issue origins and rankings. These additions will allow readers to evaluate potential biases and consistency, directly supporting the claims. revision: yes
Referee: [Evaluation] Evaluation section (comparison paragraph): The statement that IssueSpecter and CoverUp were run “under identical evaluation conditions, using the same model and the same number of evaluated artifacts per project” is load-bearing for the 81.0% vs. 76.2% claim, yet the text does not confirm that the identical set of uncovered segments was used or that bug-validity labeling followed the same criteria for both tools.

Authors: We agree that the comparison paragraph requires explicit clarification to substantiate the 81.0% versus 76.2% bug validity rates. Although the manuscript references identical conditions, the same model, and the same number of artifacts, it does not explicitly confirm use of the identical uncovered segments or uniform labeling criteria. In the revision, we will update the comparison paragraph to state: 'IssueSpecter and CoverUp were evaluated on the exact same set of uncovered code segments identified via coverage analysis across the 13 projects. Bug validity labeling for outputs from both tools was performed using the identical annotation protocol and criteria described in the Manual Annotation Protocol subsection.' This will make the fairness of the head-to-head comparison transparent while preserving the reported results. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical evaluation rests on external projects and manual labels

full rationale

The paper describes a tool (IssueSpecter) that combines coverage analysis with LLM-based defect identification to generate and rank issue reports. All headline metrics (84.6% validity, P@3/MRR gains, 81.0% vs 76.2% bug-validity edge) are obtained by running the tool on 13 external open-source Python projects, producing 10,467 reports, and then manually annotating the top-130. No equations, fitted parameters, self-referential predictions, or load-bearing self-citations appear in the derivation of these results. The central claims are therefore not reduced to quantities defined inside the paper; they depend on independent artifacts (real projects) and external human judgment.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities beyond the tool itself; all technical details required to audit the claim are absent.

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