Generating aspects-classes integration testing sequences a collaboration diagram based strategy
Bibliographic record
Abstract
Aspect-oriented software development is an emerging software engineering paradigm. It provides new constructs and tools to improve separation of crosscutting concerns into single units called aspects. The aspect paradigm introduces, in fact, new abstractions in software development. AspectJ is an aspect-oriented extension for Java. Actually, 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, the aspect paradigm introduces new dimensions in terms of control and complexity. New dependencies between aspects and classes result in new testing challenges. In fact, aspects can interact with any class in a program. Interactions between aspects and classes are new sources for program faults. Object-oriented testing techniques do not cover the new dimensions introduced by aspects. Thus, new aspect-oriented testing techniques must be developed. We propose, in this paper, a new technique to generate test sequences based on the dynamic interactions between aspects and classes. We focus, in particular, on the integration of one or more aspects in a collaboration between a group of objects. The paper also introduces associated testing criteria. The proposed approach follows an iterative process.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".