2016/03/24 by Thomas Durieux, Youssef Hamadi, Durieux, Thomas +3 · 1 citation
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Software Engineering (cs.SE) #Software Testing and Debugging Techniques #cs.SE
paper · pdf · doi:10.48550/arxiv.1603.07631
arxiv created 2016/03/24 · openalex publication_date 2016/03/24 · arxiv updated 2016/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose, BanditRepair, a system that systematically explores and assesses a set of possible runtime patches. The system is grounded on so-called bandit algorithms, that are online machine learning algorithms, designed for constantly balancing exploitation and exploration. BanditRepair's runtime patches are based on modifying the execution state for repairing null dereferences. BanditRepair constantly trades the ratio of automatically handled failures for searching for new runtime patches and vice versa. We evaluate the system with 16 null dereference field bugs, where BanditRepair identifies a total of 8460 different runtime patches, which are composed of 1 up to 8 decisions (execution modifications) taken in a row. We are the first to finely characterize the search space and the outcomes of runtime repair based on execution modification.