Analyzing Change Impact in Object-Oriented Systems
Bibliographic record
Abstract
The development of software products consumes a lot of time and resources. On the other hand, these development costs are lower than maintenance costs, which represent a major concern, specially, for systems designed with recent technologies. Systems modification should be taken rigorously, and change effects must be considered. In this paper, we propose an approach, both analytical and experimental; its objective is to analyze and predict changes impacts in object-oriented (OO) systems. The method we follow consists first, to choose an existing impact model, and adapt it afterward. An impact calculation technique based on a meta-model is developed. To evaluate our approach, an empirical study was led on a real system in which a correlation hypothesis between coupling and change impact was advanced. A concrete change was done in the target system and coupling metrics were extracted from it. The hypothesis was verified with machine-learning (ML) techniques. Obtained results are interesting; they are presented and commented
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".