SCAN: An Approach to Label and Relate Execution Trace Segments
Bibliographic record
Abstract
Identifying concepts in execution traces is a task often necessary to support program comprehension or maintenance activities. Several approaches -- static, dynamic or hybrid -- have been proposed to identify cohesive, meaningful sequence of methods in execution traces. However, none of the proposed approaches is able to label such segments and to identify relations between segments of the same trace. This paper present SCAN (Segment Concept AssigNer) an approach to assign labels to sequences of methods in execution traces, and to identify relations between such segments. SCAN uses information retrieval methods and formal concept analysis to produce sets of words helping the developer to understand the concept implemented by a segment. Specifically, formal concept analysis allows SCAN to discover commonalities between segments in different trace areas, as well as terms more specific to a given segment and high level relations between segments. The paper describes SCAN along with a preliminary manual validation -- upon execution traces collected from usage scenarios of JHotDraw and ArgoUML -- of SCAN accuracy in assigning labels representative of concepts implemented by trace segments.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".