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.
Brevity is the soul of wit: Pruning long files for code generation
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 2roles
background 1polarities
background 1representative citing papers
A fully static, LLM-driven pipeline estimates the execution trace from a single failure log, prunes the test code, and ranks faulty locations at function, block, and line levels, tested on 785 industrial Python test cases.
citing papers explorer
-
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.
-
Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models
A fully static, LLM-driven pipeline estimates the execution trace from a single failure log, prunes the test code, and ranks faulty locations at function, block, and line levels, tested on 785 industrial Python test cases.