On the effect of aspect-oriented refactoring on testability of classes: A case study
Bibliographic record
Abstract
This paper aims at investigating empirically the effect of aspect-oriented refactoring on testability of classes in object-oriented software. We investigate testability from the perspective of unit testing. We performed an empirical analysis using (test suites and refactoring) data collected from a well-known open source Java software system (JHotdraw). We used, in fact, two versions of JHotDraw: the Java version (before aspect-oriented refactoring) and the AspectJ version (after aspect-oriented refactoring). To capture testability of classes, we used two metrics to quantify the corresponding JUnit test cases. JUnit test cases have been generated using a tool (CodePro). We also used object-oriented metrics to measure various source code attributes (coupling, cohesion, inheritance, complexity and size). In order to investigate the effect of aspect-oriented refactoring on testability of classes (characteristics of corresponding test cases), we used statistical tests. Results provide evidence that testability of the refactored classes has been improved.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".