Artificial Intelligence (AI) has achieved substantial success in enhancing various software testing and program analysis techniques and applications, including but not limited to static analysis, fuzz testing, GUI testing, vulnerability detection, code similarity analysis, software debloating, and patching. We often see a synergistic effect that AI models, by learning from past experience to make decisions, can notably boost conventional program analysis and software testing tasks. Hence, it is a promising direction by applying advanced machine learning techniques into suitable software engineering tasks.
Furthermore, recent years have also witnessed a substantial adoption of AI models in safety- and security-critical applications such as medical image processing, autonomous driving, aircraft control systems, machine translation, and surveillance cameras. Thus, it is also highly crucial to apply software testing and program analysis techniques to ensure the robustness, fairness, explainability, and reliability of AI models, especially when AI is applied into safety- and security-critical applications.
The AISTA workshop aims to create an opportunity for the researchers to discuss their research, share recent ideas, and present new perspectives at the intersection of AI and Software Testing/Analysis, i.e., AI for Software Testing/Analysis and Software Testing/Analysis for AI. The workshop will consist of invited talks and presentations based on research paper submissions.
For more information please consult https://ai-sta.github.io/aista21/