Fast and accurate incremental feedback for students’ software tests using selective mutation analysis

As incorporating software testing into programming assignments becomes routine, educators have begun to assess not only the correctness of students’ software, but also adequacy their tests. In practice, rely on code coverage measures, though its shortcomings are widely known. Mutation analysis is a stronger measure test adequacy, it too costly be applied beyond small programs developed in introductory courses. We demonstrate how adapt mutation provide rapid automated feedback tests for complex projects large study dataset 1389 student ranging from trivial complex. begin by showing that although state-of-the-art practical providing courses, prohibitively expensive more subsequent To reduce this cost, we use statistical procedure select subset operators maintains accuracy while minimizing cost. show with 2 operators, costs can reduced factor 2–3 negligible loss accuracy. Finally, evaluate our approach open-source and report findings may generalize educational context.