Abstract:
Automated Program Repair (APR) has emerged as a transformative technology in software
engineering, promising to reduce debugging time and improve software reliability through automatic
bug fixing. However, developers remain hesitant to adopt APR tools due to a lack of
transparency in generated patches, creating a critical gap between technical capability and practical
adoption. This study aimed to develop FIXPLAIN, an explainable APR framework that
simultaneously generates code patches and human-readable natural language explanations using
intermediate decoder embeddings. A dataset of 34,640 Java bug-fix pairs was curated from the
MegaDiff corpus, and explanations were generated using GPT-4o and Claude with consensusbased
filtering for quality assurance. FIXPLAIN employs a lightweight adapter mechanism
with cross-attention and gating to integrate APR model embeddings into explanation generation,
using LoRA-based fine-tuning while preserving repair model quality. Three evaluation
approaches were used for validation: automated metrics (BLEU, ROUGE, METEOR), comparison
with baseline models (CodeT5, LLaMA-Instruct), and human evaluation with professional
Java developers. The framework significantly outperformed baseline models across all automated
metrics, achieving BLEU scores of 17.98 compared to 3.33 for LLaMA-Instruct and
METEOR scores of 0.36. Human evaluation with ten professional Java developers confirmed
that FIXPLAIN explanations substantially improve developer trust, perceived patch correctness,
and understanding of automated fixes. The explanations effectively clarify bug root causes
and repair logic, with particular effectiveness for complex logic errors where traditional code
comparison provides insufficient insight. FIXPLAIN successfully bridges the gap between automated
program repair capabilities and developer acceptance through transparent, trustworthy
explanations. The parameter-efficient design enables practical deployment while maintaining
repair quality, opening new avenues for multi-task APR systems and developer-centric tooling
that could transform automated program repair integration in software development workflows.
