Understanding the Effectiveness of LLMs in Automated Self-Admitted Technical Debt Repayment
Bibliographic record
Abstract
Self-Admitted Technical Debt (SATD), cases where developers intentionally acknowledge suboptimal solutions in code through comments, poses a significant challenge to software maintainability. Left unresolved, SATD can degrade code quality and increase maintenance costs. While Large Language Models (LLMs) have shown promise in tasks like code generation and program repair, their potential in automated SATD repayment remains underexplored. In this paper, we identify three key challenges in training and evaluating LLMs for SATD repayment: (1) dataset representativeness and scalability, (2) removal of irrelevant SATD repayments, and (3) limitations of existing evaluation metrics. To address the first two dataset-related challenges, we adopt a language-independent SATD tracing tool and design a 10-step filtering pipeline to extract SATD repayments from repositories, resulting two large-scale datasets: 58,722 items for Python and 97,347 items for Java. To improve evaluation, we introduce two diff-based metrics, BLEU-diff and CrystalBLEU-diff, which measure code changes rather than whole code. Additionally, we propose another new metric, LEMOD, which is both interpretable and informative. Using our new benchmarks and evaluation metrics, we evaluate two types of automated SATD repayment methods: fine-tuning smaller models, and prompt engineering with five large-scale models. Our results reveal that fine-tuned small models achieve comparable Exact Match (EM) scores to prompt-based approaches but underperform on BLEU-based metrics and LEMOD. Notably, Gemma-2-9B leads in EM, addressing 10.1% of Python and 8.1% of Java SATDs, while Llama-3.1-70B-Instruct and GPT-4o-mini excel on BLEU-diff, CrystalBLEU-diff, and LEMOD metrics. Our work contributes a robust benchmark, improved evaluation metrics, and a comprehensive evaluation of LLMs, advancing research on automated SATD repayment.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".