A multi-agent framework generates compilable, structurally complex Java tests from natural-language descriptions, beating Gemini CLI by 50–78% in compilability and 38–66% in coverage overlap.
Chatunitest: a chatgpt- based automated unit test generation tool
5 Pith papers cite this work, alongside 22 external citations. Polarity classification is still indexing.
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SeGa detects 29 of 60 real business-logic bugs by generating tests from requirement-document scenarios, 22-25 more than four LLM-based baselines.
Frontier LLMs achieve only moderate performance on multi-file unit test generation, with basic executability and cascade errors common, but manual and self-error-fixing mechanisms yield measurable gains.
Proposes a context-aware generative AI framework using a continuously updated knowledge graph and delta engine for adaptive telecom test script generation.
citing papers explorer
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Sakura: An Approach for Generating Complex Tests from Natural Language Test Descriptions
A multi-agent framework generates compilable, structurally complex Java tests from natural-language descriptions, beating Gemini CLI by 50–78% in compilability and 38–66% in coverage overlap.
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Uncovering Business Logic Bugs via Semantics-Driven Unit Test Generation
SeGa detects 29 of 60 real business-logic bugs by generating tests from requirement-document scenarios, 22-25 more than four LLM-based baselines.
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MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms
Frontier LLMs achieve only moderate performance on multi-file unit test generation, with basic executability and cascade errors common, but manual and self-error-fixing mechanisms yield measurable gains.
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Context-Aware Generative AI for Automated Telecom Test Script Generation
Proposes a context-aware generative AI framework using a continuously updated knowledge graph and delta engine for adaptive telecom test script generation.
- Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models