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Identifying similar code in software systems can assist many software engineering tasks such as program understanding and software refactoring. While most approaches focus on identifying code that looks alike, some techniques aim at detecting code that functions alike. Detecting these functional clones - code that functions alike - in object oriented languages remains an open question because of the difficulty in exposing and comparing programs' functionality effectively. We propose a novel technique, In-Vivo Clone Detection, that detects functional clones in arbitrary programs by identifying and mining their inputs and outputs. The key insight is to use existing workloads to execute programs and then measure functional similarities between programs based on their inputs and outputs, which mitigates the problems in object oriented languages reported by prior work. We implement such technique in our system, HitoshiIO, which is open source and freely available. Our experimental results show that HitoshiIO detects more than 800 functional clones across a corpus of 118 projects. In a random sample of the detected clones, HitoshiIO achieves 68+% true positive rate with only 15% false positive rate. 

This chapter is based on earlier work: “Secret Ninja testing with HALO software engineering,” in 4th International Workshop on Social Software Engineering, 2011. Copyright ACM, 2011. http://doi.acm.org/10.1145/2024645.2024657. “A competitive-collaborative approach for introducing software engineering in a CS2 class”, in 26th IEEE Conference on Software Engineering Education and Training, 2013. Copyright IEEE, 2013. http://dx.doi.org/10.1109/CSEET.2013.6595235. 

For some applications, it is impossible or impractical to know what the correct output should be for an arbitrary input, making testing difficult. Many machine-learning applications for “big data”, bioinformatics and cyberphysical systems fall in this scope: they do not have a test oracle. Metamorphic Testing, a simple testing technique that does not require a test oracle, has been shown to be effective for testing such applications. We present Metamorphic Runtime Checking, a novel approach that conducts metamorphic testing of both the entire application and individual functions during a program’s execution. We have applied Metamorphic Runtime Checking to 9 machine-learning applications, finding it to be on average 170% more effective than traditional metamorphic testing at only the full application level.