אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Mutation score is widely accepted to be a reliable measurement for the effectiveness of software tests. Recent studies, however, show that mutation analysis is extremely costly and hard to use in practice. We present a novel direct prediction model of mutation score using neural networks. Relying solely on static code features that do not require compilation or execution of the tests, we predict mutation score with an accuracy better than a quintile. When we include statement coverage as a feature, our accuracy rises to about a decile. Using a similar approach, we also improve the state-of-the-art results for binary test effectiveness prediction and introduce an intuitive, easy-to-calculate set of features superior to previously studied sets. We also publish the largest data-set of mutation score data together with 130 static code features for future research. We discuss how our approach could be integrated into real-world systems, IDEs, CI tools, and testing frameworks.