SCAN: an approach to label and relate execution trace segments
Bibliographic record
Abstract
ABSTRACT Program comprehension is a prerequisite to any maintenance and evolution task. In particular, when performing feature location, developers perform program comprehension by abstracting software features and identifying the links between high‐level abstractions (features) and program elements. We present Segment Concept AssigNer (SCAN), an approach to support developers in feature location. SCAN uses a search‐based approach to split execution traces into cohesive segments. Then, it labels the segments with relevant keywords and, finally, uses formal concept analysis to identify relations among segments. In a first study, we evaluate the performances of SCAN on six Java programs by 31 participants. We report an average precision of 69% and a recall of 63% when comparing the manual and automatic labels and a precision of 63% regarding the relations among segments identified by SCAN. After that, we evaluate the usefulness of SCAN for the purpose of feature location on two Java programs. We provide evidence that SCAN (i) identifies 69% of the gold set methods and (ii) is effective in reducing the quantity of information that developers must process to locate features—reducing the number of methods to understand by an average of 43% compared to the entire execution traces. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".