# TBar: Revisiting Template-based Automated Program Repair

arXiv: https://arxiv.org/abs/1903.08409 · HTML: https://mlsanity.com/paper/1903.08409/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2019.

## Built on by (most cited first, 30 of 65)

- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPT](https://mlsanity.com/paper/2304.00385/) (2023)
- [CodeR: Issue Resolving with Multi-Agent and Task Graphs](https://mlsanity.com/paper/2406.01304/) (2024)
- [CURE: Code-Aware Neural Machine Translation for Automatic Program Repair](https://mlsanity.com/paper/2103.00073/) (2021)
- [DebugBench: Evaluating Debugging Capability of Large Language Models](https://mlsanity.com/paper/2401.04621/) (2024)
- [A Syntax-Guided Edit Decoder for Neural Program Repair](https://mlsanity.com/paper/2106.08253/) (2021)
- [Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair](https://mlsanity.com/paper/2309.00608/) (2023)
- [Conversational Automated Program Repair](https://mlsanity.com/paper/2301.13246/) (2023)
- [UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging](https://mlsanity.com/paper/2404.17153/) (2024)
- [Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review](https://mlsanity.com/paper/2307.02503/) (2023)
- [Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verification](https://mlsanity.com/paper/2412.14063/) (2024)
- [HDLdebugger: Streamlining HDL debugging with Large Language Models](https://mlsanity.com/paper/2403.11671/) (2024)
- [ITER: Iterative Neural Repair for Multi-Location Patches](https://mlsanity.com/paper/2304.12015/) (2023)
- [Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study](https://mlsanity.com/paper/2506.08311/) (2025)
- [BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense](https://mlsanity.com/paper/2301.06241/) (2023)
- [KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair](https://mlsanity.com/paper/2302.01857/) (2023)
- [COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis](https://mlsanity.com/paper/2408.05006/) (2024)
- [Aligning the Objective of LLM-based Program Repair](https://mlsanity.com/paper/2404.08877/) (2024)
- [Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning](https://mlsanity.com/paper/2301.01113/) (2023)
- [Enhanced Automated Code Vulnerability Repair using Large Language Models](https://mlsanity.com/paper/2401.03741/) (2024)
- [Enhancing Automated Program Repair with Solution Design](https://mlsanity.com/paper/2408.12056/) (2024)
- [Dissecting the SWE-Bench Leaderboards: Profiling Submitters and Architectures of LLM- and Agent-Based Repair Systems](https://mlsanity.com/paper/2506.17208/) (2025)
- [Revisiting the Plastic Surgery Hypothesis via Large Language Models](https://mlsanity.com/paper/2303.10494/) (2023)
- [Automated Program Repair: Emerging trends pose and expose problems for benchmarks](https://mlsanity.com/paper/2405.05455/) (2024)
- [Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs](https://mlsanity.com/paper/2409.10033/) (2024)
- [The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models](https://mlsanity.com/paper/2505.02931/) (2025)
- [A Comprehensive Empirical Evaluation of Agent Frameworks on Code-centric Software Engineering Tasks](https://mlsanity.com/paper/2511.00872/) (2025)
- [Exploring the Limits of ChatGPT in Software Security Applications](https://mlsanity.com/paper/2312.05275/) (2023)
- [Repair-R1: Better Test Before Repair](https://mlsanity.com/paper/2507.22853/) (2025)

## Abstract (arXiv)

We revisit the performance of template-based APR to build comprehensive knowledge about the effectiveness of fix patterns, and to highlight the importance of complementary steps such as fault localization or donor code retrieval. To that end, we first investigate the literature to collect, summarize and label recurrently-used fix patterns. Based on the investigation, we build TBar, a straightforward APR tool that systematically attempts to apply these fix patterns to program bugs. We thoroughly evaluate TBar on the Defects4J benchmark. In particular, we assess the actual qualitative and quantitative diversity of fix patterns, as well as their effectiveness in yielding plausible or correct patches. Eventually, we find that, assuming a perfect fault localization, TBar correctly/plausibly fixes 74/101 bugs. Replicating a standard and practical pipeline of APR assessment, we demonstrate that TBar correctly fixes 43 bugs from Defects4J, an unprecedented performance in the literature (including all approaches, i.e., template-based, stochastic mutation-based or synthesis-based APR).