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In machine learning, supervised classifiers are used to obtain predictions for unlabeled data by inferring prediction functions using labeled data. Supervised classifiers are widely applied in domains such as computational biology, computational physics and healthcare to make critical decisions. However, it is often hard to test supervised classifiers since the expected answers are unknown. This is commonly known as the oracle problem and metamorphic testing (MT) has been used to test such programs. In MT, metamorphic relations (MRs) are developed from intrinsic characteristics of the software under test (SUT). These MRs are used to generate test data and to verifymore »
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Testing scientific software is a difficult task due to their inherent complexity and the lack of test oracles. In addition, these software systems are usually developed by end-user developers who are not normally trained as professional software developers nor testers. These factors often lead to inadequate testing. Metamorphic testing (MT) is a simple yet effective testing technique for testing such applications. Even though MT is a well known technique in the software testing community, it is not very well utilized by the scientific software developers. The objective of this paper is to present MT as an effective technique for testingmore »
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Testing scientific software is a difficult task due to their inherent complexity and the lack of test oracles. In addition, these software systems are usually developed by end user developers who are neither normally trained as professional software developers nor testers. These factors often lead to inadequate testing. Metamorphic testing is a simple yet effective testing technique for testing such applications. Even though MT is a well-known technique in the software testing community, it is not very well utilized by the scientific software developers. The objective of this article is to present MT as an effective technique for testing scientificmore »
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Software testing is difficult to automate, especially in programs which have no oracle, or method of determining which output is correct. Metamorphic testing is a solution this problem. Metamorphic testing uses metamorphic relations to define test cases and expected outputs. A large amount of time is needed for a domain expert to determine which metamorphic relations can be used to test a given program. Metamorphic relation prediction removes this need for such an expert. We propose a method using semi-supervised machine learning to detect which metamorphic relations are applicable to a given code base. We compare this semi-supervised model withmore »
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Matrices often represent important information in scientific applications and are involved in performing complex calculations. But systematically testing these applications is hard due to the oracle problem. Metamorphic testing is an effective approach to test such applications because it uses metamorphic relations to determine whether test cases have passed or failed. Metamorphic relations are typically identified with the help of a domain expert and is a labor intensive task. In this work we use a graph kernel based machine learning approach to predict metamorphic relations for matrix calculation programs. Previously, this graph kernel based machine learning approach was used tomore »
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Metamorphic testing is a well known approach to tackle the oracle problem in software testing. This technique requires the use of source test cases that serve as seeds for the generation of follow-up test cases. Systematic design of test cases is crucial for the test quality. Thus, source test case generation strategy can make a big impact on the fault detection effectiveness of metamorphic testing. Most of the previous studies on metamorphic testing have used either random test data or existing test cases as source test cases. There has been limited research done on systematic source test case generation formore »
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