A Probabilistic Approach to Predict Changes in Object-Oriented Software Systems
Bibliographic record
Abstract
Predicting the changes in the next release of a software system has become a quest during its maintenance phase. Such a prediction can help managers to allocate resources more appropriately which results in reducing costs associated with software maintenance activities. A measure of change-proneness of a software system also provides a good understanding of its architectural stability. This research work proposes a novel approach to predict changes in an object oriented software system. The rationale behind this approach is that in a well-designed software system, feature enhancement or corrective maintenance should affect a limited amount of existing code. The goal is to quantify this aspect of quality by assessing the probability that each class will change in a future generation. Our proposed probabilistic approach uses the dependencies obtained from the UML diagrams, as well as other data extracted from source code of several releases of a software system using reverse engineering techniques. The proposed systematic approach has been evaluated on a multi-version medium size open source project namely JFlex, the fast scanner generator for Java. The obtained results indicate the simplicity and accuracy of our approach in the comparison with existing methods in the literature
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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 | 0.001 | 0.002 |
| 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 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".