SeGa extracts business semantics from requirements to generate unit tests that detect 22-25 more real-world business logic bugs than prior LLM-based methods in industrial Go projects.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.SE 2years
2026 2roles
background 1polarities
background 1representative citing papers
A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.
citing papers explorer
-
Uncovering Business Logic Bugs via Semantics-Driven Unit Test Generation
SeGa extracts business semantics from requirements to generate unit tests that detect 22-25 more real-world business logic bugs than prior LLM-based methods in industrial Go projects.
-
Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.