Software testing is an effective, yet expensive, method to improve software quality. Test automation, a potential way to reduce testing cost, has received enormous research attention recently, but the so-called “oracle problem” (how to decide the PASS/FAIL outcome of a test execution) is still a major obstacle to such cost reduction. We have extensively investigated state-of-the-art works that contribute to address this problem, from areas such as specification mining and model inference. In this paper, we compare three types of automated oracles: Data invariants, Temporal invariants, and Finite State Automata. More specifically, we study the training cost and the false positive rate; we evaluate also their fault detection capability. Seven medium to large, industrial application subjects and real faults have been used in our empirical investigation.
Automated oracles: an empirical study on cost and effectiveness
Nguyen, Duy Cu;Marchetto, Alessandro;Tonella, Paolo
2013-01-01
Abstract
Software testing is an effective, yet expensive, method to improve software quality. Test automation, a potential way to reduce testing cost, has received enormous research attention recently, but the so-called “oracle problem” (how to decide the PASS/FAIL outcome of a test execution) is still a major obstacle to such cost reduction. We have extensively investigated state-of-the-art works that contribute to address this problem, from areas such as specification mining and model inference. In this paper, we compare three types of automated oracles: Data invariants, Temporal invariants, and Finite State Automata. More specifically, we study the training cost and the false positive rate; we evaluate also their fault detection capability. Seven medium to large, industrial application subjects and real faults have been used in our empirical investigation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
