LHDiff: A Language-Independent Hybrid Approach for Tracking Source Code Lines
Why this work is in the frame
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Bibliographic record
Abstract
Tracking source code lines between two different versions of a file is a fundamental step for solving a number of important problems in software maintenance such as locating bug introducing changes, tracking code fragments or defects across versions, merging file versions, and software evolution analysis. Although a number of such approaches are available in the literature, their performance is sensitive to the kind and degree of source code changes. There is also a marked lack of study on the effect of change types on source location tracking techniques. In this paper, we propose a language-independent technique, LHDiff, for tracking source code lines across versions that leverages simhash technique together with heuristics to improve accuracy. We evaluate our approach against state-of-the- art techniques using benchmarks containing different degrees of changes where files are selected from real world applications. We further evaluate LHDiff with other techniques using a mutation based analysis to understand how different types of changes affect their performance. The results reveal that our technique is more effective than language-independent approaches and no worse than some language-dependent techniques. In our study LHDiff even shows better performance than a state-of-the-art language- dependent approach. In addition, we also discuss limitations of different line tracking techniques including ours and propose future research directions.
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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 | 0.000 |
| 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