Discovering, reporting, and fixing performance bugs
Bibliographic record
Abstract
Software performance is critical for how users perceive the quality of software products. Performance bugs - programming errors that cause significant performance degradation - lead to poor user experience and low system throughput. Designing effective techniques to address performance bugs requires a deep understanding of how performance bugs are discovered, reported, and fixed. In this paper, we study how performance bugs are discovered, reported to developers, and fixed by developers, and compare the results with those for non-performance bugs. We study performance and non-performance bugs from three popular code bases: Eclipse JDT, Eclipse SWT, and Mozilla. First, we find little evidence that fixing performance bugs has a higher chance to introduce new functional bugs than fixing non-performance bugs, which implies that developers may not need to be over-concerned about fixing performance bugs. Second, although fixing performance bugs is about as error-prone as fixing nonperformance bugs, fixing performance bugs is more difficult than fixing non-performance bugs, indicating that developers need better tool support for fixing performance bugs and testing performance bug patches. Third, unlike many non-performance bugs, a large percentage of performance bugs are discovered through code reasoning, not through users observing the negative effects of the bugs (e.g., performance degradation) or through profiling. The result suggests that techniques to help developers reason about performance, better test oracles, and better profiling techniques are needed for discovering performance bugs.
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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 | 0.010 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".