Dynamic feature traces: finding features in unfamiliar code
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
This paper introduces an automated technique for feature location: helping developers map features to relevant source code. Like several other automated feature location techniques, ours is based on execution-trace analysis. We hypothesize that these techniques, which rely on making binary judgments about a code element's relevance to a feature, are overly sensitive to the quality of the input. The main contribution of this paper is to provide a more robust alternative, whose most distinguishing characteristic is that it employs ranking heuristics to determine a code element's relevance to a feature. We believe that our technique is less sensitive with respect to the quality of the input and we claim that it is more effective when used by developers unfamiliar with the target system. We validate our claim by applying our technique to three systems with comprehensive test suites. A developer unfamiliar with the target system spent a limited amount of effort preparing the test suite for analysis. Our results show that under these circumstances our ranking-based technique compares favorably to a technique based on binary judgements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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