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

arXiv: https://arxiv.org/abs/2408.11324 · HTML: https://mlsanity.com/paper/2408.11324/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2024.

## Built on by (most cited first, 30 of 38)

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- [MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms](https://mlsanity.com/paper/2502.06556/) (2025)
- [Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models](https://mlsanity.com/paper/2502.16071/) (2025)
- [Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality](https://mlsanity.com/paper/2503.05860/) (2025)
- [Nexus: Execution-Grounded Multi-Agent Test Oracle Synthesis](https://mlsanity.com/paper/2510.26423/) (2025)
- [Clarifying Semantics of In-Context Examples for Unit Test Generation](https://mlsanity.com/paper/2510.01994/) (2025)
- [Improving Retrieval-Augmented Deep Assertion Generation via Joint Training](https://mlsanity.com/paper/2502.10696/) (2025)
- [Large Language Models for Software Testing: A Research Roadmap](https://mlsanity.com/paper/2509.25043/) (2025)
- [FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generation](https://mlsanity.com/paper/2510.19615/) (2025)
- [Harden and Catch for Just-in-Time Assured LLM-Based Software Testing: Open Research Challenges](https://mlsanity.com/paper/2504.16472/) (2025)
- [Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models](https://mlsanity.com/paper/2509.23812/) (2025)
- [LSPRAG: LSP-Guided RAG for Language-Agnostic Real-Time Unit Test Generation](https://mlsanity.com/paper/2510.22210/) (2025)
- [CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning](https://mlsanity.com/paper/2601.22803/) (2026)
- [Breaking, Stale, or Missing? Benchmarking Coding Agents on Project-Level Test Evolution](https://mlsanity.com/paper/2605.06125/) (2026)
- [SCATE: Learning to Supervise Coding Agents for Cost-Effective Test Generation](https://mlsanity.com/paper/2607.08983/) (2026)
- [ReProAgent: Tool-Augmented Multi-Stage Agentic Generation of Bug Reproduction Tests from Issue Reports](https://mlsanity.com/paper/2607.09123/) (2026)
- [Test-driven Software Experimentation with LASSO: an LLM Prompt Benchmarking Example](https://mlsanity.com/paper/2410.08911/) (2024)
- [CUBETESTERAI: Automated JUnit Test Generation using the LLaMA Model](https://mlsanity.com/paper/2504.15286/) (2025)
- [Automated Network Protocol Testing with LLM Agents](https://mlsanity.com/paper/2510.13248/) (2025)
- [DUET: Agentic Design Understanding via Experimentation and Testing](https://mlsanity.com/paper/2512.06247/) (2025)

## 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.