Automated State-Based Unit Testing for Aspect-Oriented Programs: A Supporting Framework.
Bibliographic record
Abstract
Interactions between aspects and classes are a new source for faults. Existing objectoriented testing techniques are not adequate for testing aspect-oriented programs. As a consequence, new testing techniques must be developed. We present, in this paper, a state-based unit testing technique for aspect-oriented programs and associated tool (AJUnit). The technique focuses on the integration of one or several aspects to a class. The objective is to ensure that the integration is done without affecting the original behavior of the class. AJUnit, based on the model of JUnit, generates testing sequences covering an aspect(s)-class block of code. It also supports the execution and verification of the generated sequences. We focus on AspectJ programs. Testing an aspect(s)-class block is done incrementally. Furthermore, the generated sequences are archived. In the case of a change instantiated on a class or on one of its related aspects, only the testing sequences corresponding to the affected parts of the code are retested. The same approach is followed when introducing a new aspect influencing the class under test. The technique is based on several testing criteria that we defined. The generation and verification process of the testing sequences is completely automated.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".