An Empirical Study of the Effect of File Editing Patterns on Software Quality
Bibliographic record
Abstract
While some developers like to work on multiple code change requests, others might prefer to handle one change request at a time. This juggling of change requests and the large number of developers working in parallel often lead to files being edited as part of different change requests by one or several developers. Existing research has warned the community about the potential negative impacts of some file editing patterns on software quality. For example, when several developers concurrently edit a file as part of different change requests, they are likely to introduce bugs due to limited awareness of other changes. However, very few studies have provided quantitative evidence to support these claims. In this paper, we identify four file editing patterns. We perform an empirical study on three open source software systems to investigate the individual and the combined impact of the four patterns on software quality. We find that: (1) files that are edited concurrently by many developers have on average 2.46 times more future bugs than files that are not concurrently edited, (2) files edited in parallel with other files by the same developer have on average 1.67 times more future bugs than files individually edited, (3) files edited over an extended period (i.e., above the third quartile) of time have 2.28 times more future bugs than other files, and (4) files edited with long interruptions (i.e., above the third quartile) have 2.1 times more future bugs than other files. When more than one editing patterns are followed by one or many developers during the editing of a file, we observe that the number of future bugs in the file can be as high as 1.6 times the average number of future bugs in files edited following a single editing pattern. These results can be used by software development teams to warn developers about risky file editing patterns.
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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 | 0.011 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".