Aug 2026· International Conference on Automated Software Engineering· Vol 33· 0 citations· 29 references
TL;DR
Experimental results indicate that the dynamic validation mechanism and minimal target repair strategy can reduce invalid generation while improving the executability, assertion effectiveness, and fault-revealing capability of generated tests.
This paper addresses automated unit test generation with large language models (LLMs). LLM-based test generation has not yet attained a quality level sufficient for practical use in industry. Although LLMs often reproduce API syntax faithfully, they frequently disregard semantic usage constraints and execution-environm...
TATG introduces a unified objective representation that captures testing requirements derived from static analysis and dynamic feedback that enables fine-grained tracking of satisfied and unresolved requirements throughout generation.
Guancheng Wang, Qinghua Xu, Lionel C. Briand· 0 citations
In fast-evolving software systems, effective 'natural language requirements parsing' and downstream change effect analysis capability across a multitude of codes represents low-hanging-fruit in this regard. We present a structured framework to deploy Large Language Models (LLMs) for automating two essential software en...
Nithya Krishnan, Kumaran Ramanujam, Suresh Babu Narra et al.· 2026 International Conferenc...· 0 citations
The results suggest that organizing real-bug mechanisms as retrievable and executable feedback targets is an effective way to guide generated tests toward bug-triggering inputs and behavioral oracles.
Test4Py is presented, a novel framework that enhances type correctness in automated test generation for Python by leveraging the program’s call graph to capture richer contextual information about parameters, and introducing a behavior-based type inference mechanism that accurately infers parameter types and constructs...
Runlin Liu, Zhe Zhang, Yunge Hu et al.· ACM Transactions on Software...· 0 citations
TestAgent, a multi-agent tool implemented as a VS Code extension that automates the generation of high-quality unit tests for Java projects using repository-level Code Knowledge Graphs, demonstrates its practical utility for regression testing and bug discovery.
Ye Shang, Quanjun Zhang, Zheng Zhan et al.· SIGSOFT FSE Companion· 0 citations
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