A Change Impact Analysis Model for Aspect Oriented Programs
Bibliographic record
Abstract
Software change impact analysis (IA) plays a crucial role in software evolution. IA aims at identifying the possible effects of a source code modification. It is often used to evaluate the effects of a change after its implementation. However, more proactive approaches use IA to predict the potential effects of a change before it is implemented. In this way, IA provides useful information that can be used, among others, to guide the implementation of the change and to support regression tests selection. This paper aims at proposing a change impact analysis model for AspectJ programs. Aspect-Oriented Programming (AOP) is a natural extension of Object-Oriented Programming (OOP). It particularly promotes improved separation of crosscutting concerns into single units called aspects. The IA techniques proposed for object-oriented programs are not directly applicable for aspect-oriented programs due to the new dependencies introduced by aspects. The proposed model was designed to particularly support predictive IA. The model includes several impact rules based on the AspectJ language constructs. We performed an empirical evaluation of the model using several AspectJ programs. In order to assess the model prediction quality, we used two traditional measures: precision and recall. The reported results show that the model is able to achieve high accuracy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".