Revisiting the Performance Evaluation of Automated Approaches for the Retrieval of Duplicate Issue Reports
Bibliographic record
Abstract
Issue tracking systems (ITSs), such as Bugzilla, are commonly used to track reported bugs, improvements and change requests for a software project. To avoid wasting developer resources on previously-reported (i.e., duplicate) issues, it is necessary to identify such duplicates as soon as they are reported. Several automated approaches have been proposed for retrieving duplicate reports, i.e., identifying the duplicate of a new issue report in a list of$n$candidates. These approaches rely on leveraging the textual, categorical, and contextual information in previously-reported issues to decide whether a newly-reported issue has previously been reported. In general, these approaches are evaluated using data that spans a relatively short period of time (i.e., the classical evaluation). However, in this paper, we show that the classical evaluation tends to overestimate the performance of automated approaches for retrieving duplicate issue reports. Instead, we propose a realistic evaluation using all the reports that are available in the ITS of a software project. We conduct experiments in which we evaluate two popular approaches for retrieving duplicate issues (BM25F and REP) using the classical and realistic evaluations. We find that for the issue tracking data of the Mozilla foundation, the Eclipse foundation and OpenOffice, the realistic evaluation shows that previously proposed approaches perform considerably lower than previously reported using the classical evaluation. As a result, we conclude that the reported performance of approaches for retrieving duplicate issue reports is significantly overestimated in literature. In order to improve the performance of the automated retrieval of duplicate issue reports, we propose to leverage the resolution field of issue reports. Our experiments show that a relative improvement in the performance of a median of 7-21.5 percent and a maximum of 19-60 percent can be achieved by leveraging the resolution field of issue reports for the automated retrieval of duplicates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".