STRICT: Information Retrieval Based Search Term Identification for\n Concept Location
Bibliographic record
Abstract
During maintenance, software developers deal with numerous change requests\nthat are written in an unstructured fashion using natural language. Such\nnatural language texts illustrate the change requirement involving various\ndomain related concepts. Software developers need to find appropriate search\nterms from those concepts so that they could locate the possible locations in\nthe source code using a search technique. Once such locations are identified,\nthey can implement the requested changes there. Studies suggest that developers\noften perform poorly in coming up with good search terms for a change task. In\nthis paper, we propose a novel technique--STRICT--that automatically identifies\nsuitable search terms for a software change task by analyzing its task\ndescription using two information retrieval (IR) techniques-- TextRank and\nPOSRank. These IR techniques determine a term's importance based on not only\nits co-occurrences with other important terms but also its syntactic\nrelationships with them. Experiments using 1,939 change requests from eight\nsubject systems report that STRICT can identify better quality search terms\nthan baseline terms from 52%--62% of the requests with 30%--57% Top-10\nretrieval accuracy which are promising. Comparison with two state-of-the-art\ntechniques not only validates our empirical findings and but also demonstrates\nthe superiority of our technique.\n
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".