Towards a Tool Supporting Integration Testing of Aspect-Oriented Programs.
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
Aspect-Oriented Programming is an emerging software engineering paradigm. It offers new constructs and tools improving separation of crosscutting concerns into single units called aspects. AspectJ, the most used aspect-oriented programming language, represents an extension of Java. In fact, existing object-oriented programming languages suffer from a serious limitation in modularizing adequately crosscutting concerns in a program. Many concerns crosscut several classes in an object-oriented system. However, new dependencies between aspects and classes result in new testing challenges. Interactions between aspects and classes are new sources for program faults. Moreover, existing object-oriented testing methods (unit and integration testing) are not well adapted to the aspect technology. As a consequence, new testing techniques must be developed for aspect-oriented programs. We present, in this paper, a new aspects-classes integration testing strategy and the associated tool. The adopted approach consists of two main phases: (1) static analysis: generating testing sequences based on dynamic interactions between aspects and classes, (2) dynamic analysis: verifying the execution of the selected sequences. We focus, in particular, on the integration of one or more aspects in the control of collaborating classes.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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