AI‐Based Software Defect Predictors: Applications and Benefits in a Case Study
Bibliographic record
Abstract
Software defect prediction aims to reduce software testing efforts by guiding testers through the defect‐prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real‐life setting is difficult because it requires software metrics and defect data from past projects to predict the defect‐proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time, and reduces the testing effort. We have built a learning‐based defect prediction model for a telecommunications company in the space of one year. In this study, we have briefly explained our model, presented its payoff, and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called team software process (TSP). Our results show that defect predictors can predict 87 percent of code defects, decrease inspection efforts by 72 percent, and hence reduce postrelease defects by 44 percent. Furthermore, they can be used as complementary tools for a new process implementation whose effects on testing activities are limited.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".