Bucketing failing tests via symbolic analysis

Abstract

A common problem encountered while debugging programs is the overwhelming number of test cases generated by automated test generation tools, where many of the tests are likely to fail due to same bug. Some coarse-grained clustering techniques based on point of failure (PFB) and stack hash (CSB) have been proposed to address the problem. In this work, we propose a new symbolic analysis-based clustering algorithm that uses the semantic reason behind failures to group failing tests into more “meaningful” clusters. We implement our algorithm within the KLEE symbolic execution engine; our experiments on 21 programs drawn from multiple benchmark-suites show that our technique is effective at produ..