Revisiting strategies for ordering class integration testing in the presence of dependency cycles
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Bibliographic record
Abstract
The issue of ordering class integration in the context of integration testing of object-oriented software has been discussed by a number of researchers. More specifically, strategies have been proposed to generate a test order while minimizing stubbing. Recent papers have addressed the problem of deriving an integration order in the presence of dependency cycles in the class diagram. Such dependencies represent a practical problem as they make any topological ordering of classes impossible. The paper proposes a strategy that integrates two existing methods aimed at "breaking" cycles so as to allow a topological order of classes. The first one was proposed by K.-C. Tai and F.J. Daniels (1999) and is based on assigning a higher-level order according to aggregation and inheritance relationships and a lower-level order according to associations. The second one was proposed by Y. Le Traon et al. (2000) and is based on identifying strongly connected components in the dependency graph. Among other things, the former approach may result in unnecessary stubbing whereas the latter may lead to breaking cycles by "removing" aggregation or inheritance dependencies, thus leading to complex stubbing. We propose an approach that combines some of the principles of both approaches and addresses some of their shortcomings. All approaches (principles, benefits, drawbacks) are thoroughly compared by the means of a case study, based on a real system written in Java.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it