Understanding the impact of code and process metrics on post-release defects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research studying the quality of software applications continues to grow rapidly with researchers building regression models that combine a large number of metrics. However, these models are hard to deploy in practice due to the cost associated with collecting all the needed metrics, the complexity of the models and the black box nature of the models. For example, techniques such as PCA merge a large number of metrics into composite metrics that are no longer easy to explain. In this paper, we use a statistical approach recently proposed by Cataldo et al. to create explainable regression models. A case study on the Eclipse open source project shows that only 4 out of the 34 code and process metrics impacts the likelihood of finding a post-release defect. In addition, our approach is able to quantify the impact of these metrics on the likelihood of finding post-release defects. Finally, we demonstrate that our simple models achieve comparable performance over more complex PCA-based models while providing practitioners with intuitive explanations for its predictions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it