arxiv: 2605.11790 · v1 · submitted 2026-05-12 · 💻 cs.SE

Recognition: no theorem link

An Extensive Replication Study of the ABLoTS Approach for Bug Localization

Feifei Niu , Enshuo Zhang , Christoph Mayr-Dorn , Wesley Klewerton Guez Assun\c{c}\~ao , Liguo Huang , Jidong Ge , Bin Luo , Alexander Egyed

Authors on Pith no claims yet

Pith reviewed 2026-05-13 05:45 UTC · model grok-4.3

classification 💻 cs.SE

keywords bug localizationreplication studydata leakageinformation retrievalABLoTSTraceScoretemporal splitempirical software engineering

0 comments

The pith

A replication of ABLoTS bug localization cannot reproduce the original results because an incorrect cut-off date allowed test data to leak into training.

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

The paper replicates ABLoTS, an information-retrieval approach for recommending source-code files that contain the cause of a bug. Its core component, TraceScore, links feature requests and bug reports through traceability information to score code snippets. When run on the original Java dataset plus two larger extended datasets, TraceScore produces comparable or stronger results. Yet the replication fails to match the performance numbers published for ABLoTS. The discrepancy traces to a single overlooked choice: the original evaluation selected a cut-off date that placed later bug reports into the training set, letting test data leak forward and inflate measured accuracy.

Core claim

The central claim is that the promising results reported for ABLoTS cannot be reproduced because the original study chose a cut-off date that caused test data to leak into the training data, producing significantly inflated performance figures on the 11-project Java corpus of 8,494 bug reports.

What carries the argument

The temporal cut-off date used to split bug reports into training and test sets; an earlier date than the one implicitly used in the original work prevents future reports from contaminating the training corpus.

If this is right

TraceScore produces competitive results when evaluated on larger, temporally clean datasets without leakage.
Bug-localization performance numbers obtained with non-strict temporal splits are unreliable and cannot be compared across studies.
Any IRBL technique that trains on version-history or traceability data must enforce a forward-only cut-off to avoid the same leakage artifact.

Where Pith is reading between the lines

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

Many earlier IRBL papers may contain similar hidden temporal leakage and would show lower performance under a correct split.
Practitioners adopting bug-localization tools should treat published accuracy figures as upper bounds until the same methods are re-evaluated with strict time ordering.
Future replication or benchmark suites for bug localization should publish both the exact cut-off date and the script that enforces it.

Load-bearing premise

The replication faithfully recreated the original ABLoTS code and evaluation pipeline except for the identified cut-off date choice.

What would settle it

Re-run the original ABLoTS pipeline on the 11-project dataset while enforcing a strict cut-off date that keeps every test bug report strictly after the last training report and verify whether the reported performance metrics fall to the levels obtained in the replication.

read the original abstract

Bug localization is the task of recommending source code locations (typically files) that contain the cause of a bug and hence need to be changed to fix the bug. Along these lines, information retrieval-based bug localization (IRBL) approaches have been adopted, which identify the most bug-prone files from the source code space. In current practice, a series of state-of-the-art IRBL techniques leverage the combination of different components (e.g., similar reports, version history, and code structure) to achieve better performance. ABLoTS is a recently proposed approach with the core component, TraceScore, that utilizes requirements and traceability information between different issue reports (i.e., feature requests and bug reports) to identify buggy source code snippets with promising results. To evaluate the accuracy of these results and obtain additional insights into the practical applicability of ABLoTS, we conducted a replication study of this approach with the original dataset and also on two extended datasets (i.e., additional Java dataset and Python dataset). The original dataset consists of 11 open source Java projects with 8,494 bug reports. The extended Java dataset includes 16 more projects comprising 25,893 bug reports and corresponding source code commits. The extended Python dataset consists of 12 projects with 1,289 bug reports. While we find that the TraceScore component, which is the core of ABLoTS, produces comparable or even better results with the extended datasets, we also find that we cannot reproduce the ABLoTS results, as reported in its original paper, due to an overlooked side effect of incorrectly choosing a cut-off date that led to test data leaking into training data with significant effects on performance.

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

This replication flags a concrete data-leakage flaw in ABLoTS evaluations but does not fully isolate the cut-off date as the sole cause of the reported non-reproducibility.

read the letter

The main takeaway is that ABLoTS results cannot be reproduced because an incorrect cut-off date allowed test data to leak into training, inflating the original numbers. The authors show this effect across the original 11 Java projects plus 16 additional ones and a new set of 12 Python projects. TraceScore, the core component, still delivers comparable or better performance on the larger collections, which is a useful positive signal for the approach itself. Spotting and measuring the leakage from temporal splitting is the clearest new diagnostic here; prior work on IR-based bug localization has not called out this exact mechanism with quantified impact on the same datasets. The experiments are straightforward and the abstract makes the central claim easy to follow. The soft spot is the missing control: the paper does not report running their pipeline with only the corrected cut-off date on the original 11-project set and confirming that MAP, MRR, and other metrics then match the ABLoTS paper within reasonable tolerance. Without that check, other unstated differences in preprocessing, TraceScore implementation, or ranking could still explain part of the gap. The assumption that everything else was recreated faithfully is plausible but not directly verified in the text. This paper is for researchers who build or evaluate IR-based bug localization tools and want to avoid common temporal-split mistakes. It does not change the broader field but raises the bar for credible claims in this sub-area. The work shows clear thinking and honest engagement with the prior paper, so it deserves a serious referee even if revisions are needed to tighten the isolation of the leakage effect. I would send it to peer review.

Referee Report

1 major / 2 minor

Summary. The manuscript reports an extensive replication of the ABLoTS bug localization approach, evaluating its TraceScore component on the original 11 Java projects (8,494 bug reports) and two extended datasets: 16 additional Java projects (25,893 bug reports) and 12 Python projects (1,289 bug reports). The key finding is that while TraceScore yields comparable or superior results on the extended datasets, the original reported performance cannot be reproduced because of test data leakage into training data caused by an incorrect cut-off date selection.

Significance. If the leakage diagnosis holds, the work is significant for exposing how temporal cut-off choices can invalidate IR-based bug localization results and for demonstrating that TraceScore remains viable on larger, more recent datasets. It provides a concrete, testable explanation for non-reproducibility and encourages stricter data-handling standards in the field.

major comments (1)

[§5] §5 (Results on Original Dataset): The central claim that non-reproducibility is caused solely by the incorrect cut-off date is not load-bearingly verified; the manuscript does not report a controlled re-run of the replication pipeline on the original 11-project dataset using the corrected cut-off date to confirm that MAP/MRR values are restored to the levels published in the ABLoTS paper.

minor comments (2)

[Abstract] Abstract: The quantitative magnitude of the performance drop attributable to leakage (e.g., exact delta in MAP or MRR) is not stated, which would strengthen the claim.
[§3] The manuscript should clarify whether the extended datasets preserve the same temporal ordering and commit quality standards as the original 11 projects.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We appreciate the referee's thorough review and the recognition of the significance of our findings on data leakage in bug localization studies. We address the major comment point by point below.

read point-by-point responses

Referee: [§5] §5 (Results on Original Dataset): The central claim that non-reproducibility is caused solely by the incorrect cut-off date is not load-bearingly verified; the manuscript does not report a controlled re-run of the replication pipeline on the original 11-project dataset using the corrected cut-off date to confirm that MAP/MRR values are restored to the levels published in the ABLoTS paper.

Authors: We acknowledge that the current manuscript relies on our diagnosis of the cut-off date error and the resulting data leakage without providing a direct controlled experiment that re-runs the pipeline with the corrected date to restore the original performance. This verification would indeed strengthen the causal claim. In the revised manuscript, we will include the results of such a controlled re-run on the original 11-project dataset, demonstrating that the MAP and MRR values are restored to levels comparable to the ABLoTS paper when the leakage is eliminated. This will be added to §5. revision: yes

Circularity Check

0 steps flagged

No circularity: external replication against independent prior work

full rationale

This is an empirical replication study that compares its recreated pipeline and results against the independent ABLoTS paper. No equations, parameters, or predictions are derived from the present paper's own outputs or fits; the central claim (non-reproducibility due to cut-off date leakage) rests on direct empirical mismatch with the external original results rather than any self-referential reduction. The extended datasets and TraceScore evaluations are presented as new observations, not as quantities defined by the paper's conclusions. No self-citation load-bearing, ansatz smuggling, or renaming of known results occurs in the derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The replication rests on standard assumptions in empirical software engineering: that bug reports and commits can be temporally ordered without leakage, that file-level granularity is appropriate for localization, and that standard IR metrics (e.g., MAP, MRR) are comparable across studies. No new free parameters or invented entities are introduced.

axioms (2)

domain assumption Temporal ordering of bug reports and commits must be respected to avoid future information leaking into training data.
Invoked when diagnosing the original cut-off date error.
domain assumption File-level bug localization is a valid proxy for the practical task of identifying code that must be changed.
Standard assumption in IRBL literature that the replication inherits.

pith-pipeline@v0.9.0 · 5638 in / 1391 out tokens · 82738 ms · 2026-05-13T05:45:17.587080+00:00 · methodology

discussion (0)

Reference graph

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