Summary Runtime verification (RV) helps to find software bugs by monitoring formally specified properties during testing. A key problem in using RV during testing is how to reduce the manual inspection effort for checking whether property violations are true bugs. To date, there was no automated approach for determining the likelihood that property violations were true bugs to reduce tedious and time‐consuming manual inspection. We present RVprio, the first automated approach for prioritizing RV violations in order of likelihood of being true bugs. RVprio uses machine learning classifiers to prioritize violations. For training, we used a labelled dataset of 1170 violations from 110 projects. On that dataset, (1) RVprio reached 90% of the effectiveness of a theoretically optimal prioritizer that ranks all true bugs at the top of the ranked list, and (2) 88.1% of true bugs were in the top 25% of RVprio‐ranked violations; 32.7% of true bugs were in the top 10%. RVprio was also effective when we applied it to new unlabelled violations, from which we found previously unknown bugs—54 bugs in 8 open‐source projects. Our dataset is publicly available online.

Runtime Verification (RV) can help find software bugs by monitoring formally specified properties during testing. A key problem when using RV during testing is how to reduce the manual inspection effort for checking whether property violations are true bugs. To date, there was no automated approach for determining the likelihood that property violations were true bugs to reduce tedious and time-consuming manual inspection.We present RVPRIO, the first automated approach for prioritizing RV violations in order of likelihood of being true bugs. RVPRIO uses machine learning classifiers to prioritize violations. For training, we used a labeled dataset of 1,170 violations from 110 projects. On that dataset, (1) RVPRIO reached 90% of the effectiveness of a theoretically optimal prioritizer that ranks all true bugs at the top of the ranked list, and (2) 88.1% of true bugs were in the top 25% of RVPRIO-ranked violations; 32.7% of true bugs were in the top 10%. RVPRIO was also effective when we applied it to new unlabeled violations, from which we found previously unknown bugs-29 bugs in 7 projects and two bugs in two properties. Our dataset is publicly available online.