Bibliographic record
Abstract
Modern source-level debuggers support dynamic breakpoints that are guarded by conditions based on program state. Such breakpoints address situations where a static breakpoint is not sufficiently precise to characterise a point of interest in program execution. However, we believe that current IDE support for dynamic breakpoints are cumbersome to use. Firstly, guard conditions formulated in (non-aspect-oriented) source-languages cannot directly express control-flow conditions, forcing developers to seek alternative formulations. Secondly, guard-conditions can be complex expressions and manually typing them is cumbersome.We present the Control-flow Breakpoint Debugger (CBD). CBD uses a dynamic pointcut language to characterise control-flow breakpoints---dynamic breakpoints which are conditional on the control-flow through which they were reached. CBD provides a "point-and-click" GUI to specify and incrementally refine control-flow breakpoints, thereby avoiding the burden of manually editing the potentially complex expressions that define them.We performed 20 case studies debugging and fixing documented bugs in 3 existing applications. Our results show that dynamic breakpoints in general are useful in practice, and that CBD's GUI allows specifying them adequately in the majority of cases.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".