mlsanityData to 7 Oct 2026

HITS: High-coverage LLM-based Unit Test Generation via Method Slicing

arXiv 2408.11324 ↗First posted 21 Aug 2024cs.SEcited by 37

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Abstract (arXiv)

Large language models (LLMs) have behaved well in generating unit tests for Java projects. However, the performance for covering the complex focal methods within the projects is poor. Complex methods comprise many conditions and loops, requiring the test cases to be various enough to cover all lines and branches. However, existing test generation methods with LLMs provide the whole method-to-test to the LLM without assistance on input analysis. The LLM has difficulty inferring the test inputs to cover all conditions, resulting in missing lines and branches. To tackle the problem, we propose decomposing the focal methods into slices and asking the LLM to generate test cases slice by slice. Our method simplifies the analysis scope, making it easier for the LLM to cover more lines and branches in each slice. We build a dataset comprising complex focal methods collected from the projects used by existing state-of-the-art approaches. Our experiment results show that our method significantly outperforms current test case generation methods with LLMs and the typical SBST method Evosuite regarding both line and branch coverage scores.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Aug 2024.

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Citing papers per year: 2024: 1; 2025: 24; 2026: 12.

37 papers.

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  1. 2025Tool-integrated Reinforcement Learning for Repo Deep Searchcited by 14
  2. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 13
  3. 2025A Contemporary Survey of Large Language Model Assisted Program Analysiscited by 10
  4. 2025LLM Test Generation via Iterative Hybrid Program Analysiscited by 9
  5. 2025TestForge: Feedback-Driven, Agentic Test Suite Generationcited by 8
  6. 2025Hallucination to Consensus: Multi-Agent LLMs for End-to-End JUnit Test Generationcited by 8
  7. 2025VeriStruct: AI-assisted Automated Verification of Data-Structure Modules in Veruscited by 8
  8. 2025Evaluating LLM-Based Regression Test Generationcited by 6
  9. 2025REFINE: Enhancing Program Repair Agents through Context-Aware Patch Refinementcited by 6
  10. 2026Test vs Mutant: Adversarial LLM Agents for Robust Unit Test Generationcited by 6
  11. 2025Enhancing Project-Specific Code Completion by Inferring Internal API Informationcited by 5
  12. 2025MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanismscited by 4
25 more
  1. 2025Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Modelscited by 4
  2. 2025Nexus: Execution-Grounded Multi-Agent Test Oracle Synthesiscited by 4
  3. 2025Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Qualitycited by 3
  4. 2025Clarifying Semantics of In-Context Examples for Unit Test Generationcited by 3
  5. 2025Improving Retrieval-Augmented Deep Assertion Generation via Joint Trainingcited by 2
  6. 2025Large Language Models for Software Testing: A Research Roadmapcited by 2
  7. 2025FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generationcited by 2
  8. 2025Harden and Catch for Just-in-Time Assured LLM-Based Software Testing: Open Research Challengescited by 1
  9. 2025Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Modelscited by 1
  10. 2025LSPRAG: LSP-Guided RAG for Language-Agnostic Real-Time Unit Test Generationcited by 1
  11. 2026Breaking, Stale, or Missing? Benchmarking Coding Agents on Project-Level Test Evolutioncited by 1
  12. 2026SCATE: Learning to Supervise Coding Agents for Cost-Effective Test Generationcited by 1
  13. 2026ReProAgent: Tool-Augmented Multi-Stage Agentic Generation of Bug Reproduction Tests from Issue Reportscited by 1
  14. 2024Test-driven Software Experimentation with LASSO: an LLM Prompt Benchmarking Examplecited by 0
  15. 2025CUBETESTERAI: Automated JUnit Test Generation using the LLaMA Modelcited by 0
  16. 2025Automated Network Protocol Testing with LLM Agentscited by 0
  17. 2025DUET: Agentic Design Understanding via Experimentation and Testingcited by 0
  18. 2026SWE-Spot: Building Small Repo-Experts with Repository-Centric Learningcited by 0
  19. 2026CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learningcited by 0
  20. 2026Call-Chain-Aware LLM-Based Test Generation for Java Projectscited by 0
  21. 2026ConCovUp: Effective Agent-Based Test Driver Generation for Concurrency Testingcited by 0
  22. 2026MuMuTestUp: Mutation-based Multi-Agent Test Case Updatecited by 0
  23. 2026Multi-Agent LLM Collaboration for Unit Test Generation via Human-Testing-Inspired Workflowscited by 0
  24. 2026Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Testscited by 0
  25. 2026Generation of Web Apps with Agentic IDEs: An Empirical Assessmentcited by 0