Generating Unit Test Sequences for Aspect-Oriented Programs: Towards a Formal Approach Using UML State Diagrams
Bibliographic record
Abstract
Aspect-oriented programming is an emerging software engineering paradigm that improves separation of crosscutting concerns in a program. Existing object-oriented programming languages suffer from a serious limitation in modularizing adequately crosscutting concerns. Many concerns crosscut several classes in an object-oriented system. However, in spite of the many claimed benefits that the aspect paradigm seems to be offering, it remains clear that it is not yet mature. Aspect technology introduces, in fact, new dimensions in terms of control and complexity. Moreover, aspects have great latitude to interact with basic classes of a system. Those interactions constitute a new source for faults in a program. Existing object-oriented testing techniques are not adequate for testing aspect-oriented programs. Thus, new testing techniques must be developed for aspect-oriented programs. We present, in this paper, a new technique for aspect-oriented unit testing based on dynamic behavior. We also introduce several testing criteria. We focus on the integration of one or more aspects to a class. The proposed technique is based on UML statecharts. The primary objective is to verify that this integration is done correctly, without modifying the original behavior of the basic class.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".