Towards a more efficient static software change impact analysis method
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
Impact analysis methods are commonly employed to reduce the likelihood of encountering faulty or unexpected behavior from a software program as a result of developers' oblivious modifications. In this paper, we propose a static impact analysis technique that creates clusters of closely associated software program files based on their co-modification history in the software repository. The proposed method benefits from dimensionality reduction techniques to reduce the complexity of the collected information and perform the impact analysis process faster. The method has been tested on four different open source project repositories, namely Firefox, Firebird, Thunderbird, and FileZilla. The results of the impact analysis method performance in terms of precision (impact set identification accuracy) and execution time cost have been reported in this paper. The proposed method shows promising behavior when used with several specific clustering techniques such as DBscan and X-Means.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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