Predicting the Change Impact of Resolving Defects by Leveraging the Topics of Issue Reports in Open Source Software Systems
Bibliographic record
Abstract
Upon receiving a new issue report, practitioners start by investigating the defect type, the potential fixing effort needed to resolve the defect and the change impact. Moreover, issue reports contain valuable information, such as, the title, description and severity, and researchers leverage the topics of issue reports as a collective metric portraying similar characteristics of a defect. Nonetheless, none of the existing studies leverage the defect topic, i.e., a semantic cluster of defects of the same nature, such as Performance, GUI, and Database , to estimate the change impact that represents the amount of change needed in terms of code churn and the number of files changed. To this end, in this article, we conduct an empirical study on 298,548 issue reports belonging to three large-scale open-source systems, i.e., Mozilla, Apache, and Eclipse, to estimate the change impact in terms of code churn or the number of files changed while leveraging the topics of issue reports. First, we adopt the Embedded Topic Model (ETM), a state-of-the-art topic modelling algorithm, to identify the topics. Second, we investigate the feasibility of predicting the change impact using the identified topics and other information extracted from the issue reports by building eight prediction models that classify issue reports requiring small or large change impact along two dimensions, i.e., the code churn size and the number of files changed. Our results suggest that XGBoost is the best-performing algorithm for predicting the change impact, with an AUC of 0.84, 0.76, and 0.73 for the code churn and 0.82, 0.71, and 0.73 for the number of files changed metric for Mozilla, Apache, and Eclipse, respectively. Our results also demonstrate that the topics of issue reports improve the recall of the prediction model by up to 45%.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".