Hot fixes show urgency patterns with reduced collaboration and testing, differing from regular fixes, and human versus AI agents display over 10 distinct repair behaviors in large-scale GitHub data.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.SE 4representative citing papers
StarCoder2-15B matches or beats CodeLlama-34B on code tasks despite being smaller, and StarCoder2-3B outperforms prior 15B models, with open weights and exact training data identifiers released.
Empirical evaluation shows that code generated by all seven tested LLMs contains vulnerabilities, the majority of critical or high severity.
A research roadmap analyzing the current state of search-based software engineering with foundation models, outlining challenges and directions across three integration aspects.
citing papers explorer
-
Hot Fixing in the Wild
Hot fixes show urgency patterns with reduced collaboration and testing, differing from regular fixes, and human versus AI agents display over 10 distinct repair behaviors in large-scale GitHub data.
-
Security of LLM-generated Code: A Comparative Analysis
Empirical evaluation shows that code generated by all seven tested LLMs contains vulnerabilities, the majority of critical or high severity.