Enhancing Test Generation for Hard-to-Cover Branches via Program Analysis and Large Language Models
TELPA, a novel LLM-based test generation technique, leverages program analysis to enhance the coverage of hard-to-cover branches by extracting real usage scenarios, understanding inter-procedural dependencies, and guiding LLMs with counter-examples.
Code-Aware Prompting: Improving Coverage-Guided Test Generation for Complex Software Units using Large Language Models
SymPrompt, a novel code-aware prompting strategy, enables large language models to generate high coverage test suites for complex software units by deconstructing the test generation process into a multi-stage sequence of prompts aligned with the execution paths of the method under test.
Automated Synthesis of Metamorphic Relations from Existing Test Cases
Existing developer-written test cases can embed domain knowledge that encodes metamorphic relations (MRs). MR-Scout automatically discovers and synthesizes these encoded MRs from test cases in open-source software projects to enable automated test case generation.
Software Testing with Large Language Models: Survey, Landscape, and Vision
Pre-trained large language models are revolutionizing software testing, offering innovative approaches and addressing key challenges.
Mutation Analysis with Execution Taints: A Novel Technique for Efficient Mutation Analysis
Effiziente Mutation Analyse durch Ausführungstaints.
COVERUP: Coverage-Guided LLM-Based Test Generation
COVERUP is a novel system that significantly improves Python regression test coverage through an iterative, coverage-guided approach using large-language models (LLMs).
COVERUP: Coverage-Guided LLM-Based Test Generation
COVERUP is a novel system that significantly improves Python regression test coverage by combining coverage analysis and large-language models (LLMs).
Fine-Grained Assertion-Based Test Selection: Precision and Efficiency Analysis
Fine-grained assertion-based test selection improves precision and efficiency in regression testing.
Enhancing Testing at Meta with Rich-State Simulated Populations: Impact on Coverage and Fault Detection
Rich-state simulated populations significantly enhance test coverage and fault detection in automated testing at Meta.
BayesFLo: Bayesian Fault Localization of Complex Software Systems
提案されたBayesFLoフレームワークは、複雑なソフトウェアシステムのベイジアン障害特定を効率的に行います。
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