A detailed examination of the correlation between imports and failure-proneness of software components
Bibliographic record
Abstract
Research has provided evidence that type usage in source files is correlated with the risk of failure of software components. Previous studies that investigated the correlation between type usage and component failure assigned equal blame to all the types imported by a component with a failure history, regardless of whether a type is used in the component, or associated to its failures. A failure-prone component may use a type, but it is not always the case that the use of this type has been responsible for any of its failures. To gain more insight about the correlation between type usage and component failure, we introduce the concept of a failure-associated type to represent the imported types referenced within methods fixed due to failures. We conducted two studies to investigate the tradeoffs between the equal-blame approach and the failure-associated type approach. Our results indicate that few of the types or packages imported by a failure-prone component are associated with its failures - less than 25% of the type imports, and less than 55% of the packages whose usage were reported to be highly correlated with failures by the equal-blame approach, were actually correlated with failures when we looked at the failure-associated types.
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 | 0.010 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".