The core challenge of fuzzing is to consistently generate test inputs that are both syntactically valid and semantically diverse. However, mutation-based coverage-guided fuzzing often struggles to achieve this, especially for highly structured formats. Two lines of work have emerged to address this challenge: (1) hybri...
Si-Wei Wei, Shi-Hao Zhu, Rui-Jie Meng et al.· Proceedings of the 41st IEEE...· 1 citation
A three-level taxonomy inspired by autonomous driving that distinguishes degrees of autonomy along a roadmap from today’s AI-assisted development workflows to fully autonomous software development in which AI systems autonomously identify demands and design, implement, verify, and maintain software without human oversi...
The results show that witness generation effectively distinguishes inferences supported by plausible completions from those requiring unrealistic assumptions, providing a practical mechanism for validating reasoning over incomplete programs.
Zhiyuan Liu, Yihe Li, Trevor E. Carlson et al.· arXiv.org· 0 citations
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