TestSpark: IntelliJ IDEA's Ultimate Test Generation Companion
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Abstract (arXiv)
Writing software tests is laborious and time-consuming. To address this, prior studies introduced various automated test-generation techniques. A well-explored research direction in this field is unit test generation, wherein artificial intelligence (AI) techniques create tests for a method/class under test. While many of these techniques have primarily found applications in a research context, existing tools (e.g., EvoSuite, Randoop, and AthenaTest) are not user-friendly and are tailored to a single technique. This paper introduces TestSpark, a plugin for IntelliJ IDEA that enables users to generate unit tests with only a few clicks directly within their Integrated Development Environment (IDE). Furthermore, TestSpark also allows users to easily modify and run each generated test and integrate them into the project workflow. TestSpark leverages the advances of search-based test generation tools, and it introduces a technique to generate unit tests using Large Language Models (LLMs) by creating a feedback cycle between the IDE and the LLM. Since TestSpark is an open-source (https://github.com/JetBrains-Research/TestSpark), extendable, and well-documented tool, it is possible to add new test generation methods into the plugin with the minimum effort. This paper also explains our future studies related to TestSpark and our preliminary results. Demo video: https://youtu.be/0F4PrxWfiXo
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jan 2024.
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Citing papers per year: 2024: 1; 2025: 7; 2026: 1.
9 papers.
- 2025Benchmarking LLMs for Unit Test Generation from Real-World Functionscited by 8
- 2024Automating REST API Postman Test Cases Using LLMcited by 4
- 2025LSPRAG: LSP-Guided RAG for Language-Agnostic Real-Time Unit Test Generationcited by 1
- 2025LLMs for Automated Unit Test Generation and Assessment in Java: The AgoneTest Frameworkcited by 1
- 2025Unify and Triumph: Polyglot, Diverse, and Self-Consistent Generation of Unit Tests with LLMscited by 0
- 2025Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmapcited by 0
- 2025SAGE:Specification-Aware Grammar Extraction for Automated Test Case Generation with LLMscited by 0
- 2025DUET: Agentic Design Understanding via Experimentation and Testingcited by 0
- 2026ATTest: Agent-Driven Tensor Testing for Deep Learning Library Modulescited by 0
- 2026ATTest: Agent-Driven Tensor Testing for Deep Learning Library Modules15 Feb 2026
- 2025DUET: Agentic Design Understanding via Experimentation and Testing6 Dec 2025
- 2025LLMs for Automated Unit Test Generation and Assessment in Java: The AgoneTest Framework25 Nov 2025
- 2025LSPRAG: LSP-Guided RAG for Language-Agnostic Real-Time Unit Test Generation25 Oct 2025
- 2025Benchmarking LLMs for Unit Test Generation from Real-World Functions1 Aug 2025
- 2025SAGE:Specification-Aware Grammar Extraction for Automated Test Case Generation with LLMs4 Jun 2025
- 2025Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap26 May 2025
- 2025Unify and Triumph: Polyglot, Diverse, and Self-Consistent Generation of Unit Tests with LLMs20 Mar 2025
- 2024Automating REST API Postman Test Cases Using LLM16 Apr 2024