Tool support for understanding and diagnosing pointcut expressions
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
Writing correct AspectJ pointcuts is hard. This is partly because of the complexity of the pointcut language and partly because it requires understanding how a pointcut matches across the entire code base. In this thesis, we present algorithms that compute two kinds of useful information that can help AspectJ developers diagnose and fix potential problems with their pointcuts. First, we present an algorithm to compute almost matched join points. Second we present algorithms to compute explanations of why a pointcut does not match (or does match) a specific join point. We implemented two tools using these algorithms. The first is an offline tool that analyzes a code base and produces a comprehensive report. Using this tool, we were able to find several real problems in existing, medium-sized AspectJ code bases. The second tool is an Eclipse plugin called PointcutDoctor. Pointcut-Doctor is a natural extension of AJDT, the mainstream IDE for AspectJ. It provides developers easy access to the same information from within their already familiar development environment. ii Table of Contents Abstract................................. ii
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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