Improved Query Reformulation for Concept Location using CodeRank and\n Document Structures
Bibliographic record
Abstract
During software maintenance, developers usually deal with a significant\nnumber of software change requests. As a part of this, they often formulate an\ninitial query from the request texts, and then attempt to map the concepts\ndiscussed in the request to relevant source code locations in the software\nsystem (a.k.a., concept location). Unfortunately, studies suggest that they\noften perform poorly in choosing the right search terms for a change task. In\nthis paper, we propose a novel technique --ACER-- that takes an initial query,\nidentifies appropriate search terms from the source code using a novel term\nweight --CodeRank, and then suggests effective reformulation to the initial\nquery by exploiting the source document structures, query quality analysis and\nmachine learning. Experiments with 1,675 baseline queries from eight subject\nsystems report that our technique can improve 71% of the baseline queries which\nis highly promising. Comparison with five closely related existing techniques\nin query reformulation not only validates our empirical findings but also\ndemonstrates the 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".