UNDERSTANDING THE EVOLUTION AND CO-EVOLUTION OF CLASSES IN OBJECT-ORIENTED SYSTEMS
Bibliographic record
Abstract
As software systems evolve over a long time, non-trivial and often unintended relationships among system classes arise, which cannot be easily perceived through source-code reading. As a result, the developers' understanding of continuously evolving, large, long-lived systems deteriorates steadily. A most interesting relationship is class co-evolution: because of implicit design dependencies clusters of classes change in "parallel" ways and recognizing such co-evolution is crucial in effectively extending and maintaining the system. In this paper, we propose a data-mining method for recovering "hidden" co-evolutions of system classes. This method relies on our UML-aware structural differencing algorithm, UMLDiff, which, given a sequence of UML class models of an object-oriented software system, produces a sequence of "change records" that describe the design-level changes over its life span. The change records are analyzed from the perspective of each individual system class to extract "class change profiles". Each phase of a class change profile is then discretized and classified into one of two general change types: function extension or refactoring. Finally, the Apriori association-rule mining algorithm is applied to the database of categorical class change profiles, to elicit co-evolution patterns among two or more classes, which may be as yet undocumented and unknown. The recovered knowledge facilitates the overall understanding of system evolution and the planning of future maintenance activities. We report on one real world case study evaluating our approach.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".