Supporting predictive change impact analysis: a control call graph based technique
Why this work is in the frame
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Bibliographic record
Abstract
Change impact analysis plays an important role in software maintenance. It allows developers assessing the possible effects of a change. We present, in this paper, a new static technique supporting software change impact analysis. The technique uses a new model based on control call graphs. It captures the control related to components calls and generates the different control flow paths in a program. The generated paths, in a compacted form, are used to identify the potential set of components that may be affected by a given change. Furthermore, the tool developed can be used to perform predictive impact analysis. It can also be used to support regression testing. We performed an experimental study on several Java programs. The reported results show that the proposed technique can predict impact sets that are more accurate than those obtained using traditional approaches based on call graphs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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