Automated State-Based Unit Testing for Aspect-Oriented Programs: A Supporting Framework.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.006 |
| 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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 it