LHDiff: A Language-Independent Hybrid Approach for Tracking Source Code Lines
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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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".