Interpretation Conclusion Stability of Software Defect Prediction over Time
Bibliographic record
Abstract
Abstract Model instability refers to where a machine learning model trained on historical data becomes less reliable over time due to Concept Drift (CD). CD refers to the phenomenon where the underlying data distribution changes over time. In this paper, we proposed a method for predicting CD in evolving software through the identification of inconsistencies in the instance interpretation over time for the first time. To this end, we obtained the instance interpretation vector for each newly created commit sample by developers over time. Wherever there is a significant difference in statistical distribution between the interpreted sample and previously ones, it is identified as CD. To evaluate our proposed method, we have conducted a comparison of the method's results with those of the baseline method. The baseline method locates CD points by monitoring the Error Rate (ER) over time. In the baseline method, CD is identified whenever there is a significant rise in the ER. In order to extend the evaluation of the proposed method, we have obtained the CD points by the baseline method based on monitoring additional efficiency measures over time besides the ER. Furthermore, this paper presents an experimental study to investigate the discovery of CD over time using the proposed method by taking into account resampled datasets for the first time. The results of our study conducted on 20 known datasets indicated that the model's instability over time can be predicted with a high degree of accuracy without requiring the labeling of newly entered data.
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".