# Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs

arXiv: https://arxiv.org/abs/1811.04211 · HTML: https://mlsanity.com/paper/1811.04211/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2018.

## Built on by (most cited first, 30 of 32)

- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
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- [Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review](https://mlsanity.com/paper/2307.02503/) (2023)
- [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)
- [KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair](https://mlsanity.com/paper/2302.01857/) (2023)
- [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)
- [DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models](https://mlsanity.com/paper/2402.13291/) (2024)
- [Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs](https://mlsanity.com/paper/2409.10033/) (2024)
- [TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment](https://mlsanity.com/paper/2409.19894/) (2024)
- [Predicting Patch Correctness Based on the Similarity of Failing Test Cases](https://mlsanity.com/paper/2107.13296/) (2021)
- [DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information](https://mlsanity.com/paper/2512.24635/) (2025)
- [Beyond Accuracy: Behavioral Dynamics of Agentic Multi-Hunk Repair](https://mlsanity.com/paper/2511.11012/) (2025)
- [Evaluating the Impact of Source Code Parsers on ML4SE Models](https://mlsanity.com/paper/2206.08713/) (2022)
- [A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair](https://mlsanity.com/paper/2506.04987/) (2025)
- [From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMs](https://mlsanity.com/paper/2506.13182/) (2025)
- [Exploring Plausible Patches Using Source Code Embeddings in JavaScript](https://mlsanity.com/paper/2103.16846/) (2021)
- [Tea: Program Repair Using Neural Network Based on Program Information Attention Matrix](https://mlsanity.com/paper/2107.08262/) (2021)
- [NARRepair: Non-Autoregressive Code Generation Model for Automatic Program Repair](https://mlsanity.com/paper/2406.16526/) (2024)
- [MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair](https://mlsanity.com/paper/2408.09568/) (2024)
- [Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repair](https://mlsanity.com/paper/2505.07372/) (2025)
- [LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software](https://mlsanity.com/paper/2604.12994/) (2026)
- [U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning](https://mlsanity.com/paper/2605.02765/) (2026)
- [MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair](https://mlsanity.com/paper/2605.17444/) (2026)

## Abstract (arXiv)

We propose NOPOL, an approach to automatic repair of buggy conditional statements (i.e., if-then-else statements). This approach takes a buggy program as well as a test suite as input and generates a patch with a conditional expression as output. The test suite is required to contain passing test cases to model the expected behavior of the program and at least one failing test case that reveals the bug to be repaired. The process of NOPOL consists of three major phases. First, NOPOL employs angelic fix localization to identify expected values of a condition during the test execution. Second, runtime trace collection is used to collect variables and their actual values, including primitive data types and objected-oriented features (e.g., nullness checks), to serve as building blocks for patch generation. Third, NOPOL encodes these collected data into an instance of a Satisfiability Modulo Theory (SMT) problem, then a feasible solution to the SMT instance is translated back into a code patch. We evaluate NOPOL on 22 real-world bugs (16 bugs with buggy IF conditions and 6 bugs with missing preconditions) on two large open-source projects, namely Apache Commons Math and Apache Commons Lang. Empirical analysis on these bugs shows that our approach can effectively fix bugs with buggy IF conditions and missing preconditions. We illustrate the capabilities and limitations of NOPOL using case studies of real bug fixes.