mlsanityData to 7 Oct 2026

Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs

arXiv 1811.04211 ↗First posted 10 Nov 2018cs.SEcited by 32

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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.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Nov 2018.

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Citing papers per year: 2021: 5; 2022: 1; 2023: 4; 2024: 11; 2025: 6; 2026: 5.

32 papers.

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  1. 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repaircited by 225
  2. 2021CURE: Code-Aware Neural Machine Translation for Automatic Program Repaircited by 85
  3. 2021A Syntax-Guided Edit Decoder for Neural Program Repaircited by 57
  4. 2024Automatic Programming: Large Language Models and Beyondcited by 35
  5. 2023Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Reviewcited by 32
  6. 2024HDLdebugger: Streamlining HDL debugging with Large Language Modelscited by 25
  7. 2023ITER: Iterative Neural Repair for Multi-Location Patchescited by 16
  8. 2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repaircited by 13
  9. 2024Aligning the Objective of LLM-based Program Repaircited by 12
  10. 2023Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoningcited by 11
  11. 2024Enhanced Automated Code Vulnerability Repair using Large Language Modelscited by 10
  12. 2024Enhancing Automated Program Repair with Solution Designcited by 8
20 more
  1. 2025Dissecting the SWE-Bench Leaderboards: Profiling Submitters and Architectures of LLM- and Agent-Based Repair Systemscited by 8
  2. 2024DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Modelscited by 7
  3. 2024Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugscited by 7
  4. 2024TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignmentcited by 7
  5. 2021Predicting Patch Correctness Based on the Similarity of Failing Test Casescited by 4
  6. 2025DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Informationcited by 3
  7. 2025Beyond Accuracy: Behavioral Dynamics of Agentic Multi-Hunk Repaircited by 2
  8. 2022Evaluating the Impact of Source Code Parsers on ML4SE Modelscited by 1
  9. 2025A Multi-Dataset Evaluation of Models for Automated Vulnerability Repaircited by 1
  10. 2025From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMscited by 1
  11. 2021Exploring Plausible Patches Using Source Code Embeddings in JavaScriptcited by 0
  12. 2021Tea: Program Repair Using Neural Network Based on Program Information Attention Matrixcited by 0
  13. 2024NARRepair: Non-Autoregressive Code Generation Model for Automatic Program Repaircited by 0
  14. 2024MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repaircited by 0
  15. 2025Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repaircited by 0
  16. 2026LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Softwarecited by 0
  17. 2026U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planningcited by 0
  18. 2026MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repaircited by 0
  19. 2026ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repaircited by 0
  20. 2026Externalizing Requirement-to-Repair Artifacts as Observable Traces for LLM-Based Program Repaircited by 0