# A Critical Review of "Automatic Patch Generation Learned from Human-Written Patches": Essay on the Problem Statement and the Evaluation of Automatic Software Repair

arXiv: https://arxiv.org/abs/1408.2103 · HTML: https://mlsanity.com/paper/1408.2103/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2014.

## Built on by (most cited first, 4 of 4)

- [Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning](https://mlsanity.com/paper/2301.01113/) (2023)
- [Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study](https://mlsanity.com/paper/2602.00164/) (2026)
- [Exploring Plausible Patches Using Source Code Embeddings in JavaScript](https://mlsanity.com/paper/2103.16846/) (2021)
- [A Tertiary Review of Large Language Model-Based Code Generating Tasks: Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2605.25536/) (2026)

## Abstract (arXiv)

At ICSE'2013, there was the first session ever dedicated to automatic program repair. In this session, Kim et al. presented PAR, a novel template-based approach for fixing Java bugs. We strongly disagree with key points of this paper. Our critical review has two goals. First, we aim at explaining why we disagree with Kim and colleagues and why the reasons behind this disagreement are important for research on automatic software repair in general. Second, we aim at contributing to the field with a clarification of the essential ideas behind automatic software repair. In particular we discuss the main evaluation criteria of automatic software repair: understandability, correctness and completeness. We show that depending on how one sets up the repair scenario, the evaluation goals may be contradictory. Eventually, we discuss the nature of fix acceptability and its relation to the notion of software correctness.