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  1. 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 »the correctness of the test results without the presence of a test oracle. Effectiveness of MT heavily depends on the MRs used for testing. In this paper we have conducted an extensive empirical study to evaluate the fault detection effectiveness of MRs that have been used in multiple previous studies to test supervised classifiers. Our study uses a total of 709 reachable mutants generated by multiple mutation engines and uses data sets with varying characteristics to test the SUT. Our results reveal that only 14.8% of these mutants are detected using the MRs and that the fault detection effectiveness of these MRs do not scale with the increased number of mutants when compared to what was reported in previous studies.« less
  2. 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 »scientific software. To this end, we discuss why MT is an appropriate testing technique for scientists and engineers who are not primarily trained as software developers. Specifically, how it can be used to conduct systematic and effective testing on programs that do not have test oracles without requiring additional testing tools.« less
  3. 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 »software. To this end, we discuss why MT is an appropriate testing technique for scientists and engineers who are not primarily trained as software developers. Especially, how it can be used to conduct systematic and effective testing on programs that do not have test oracles without requiring additional testing tools.« less
  4. 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 »a supervised model, and show that the addition of unlabeled data improves the classification accuracy of the MR prediction model.« less
  5. 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 »successfully predict metamorphic relations for programs that perform numerical calculations. Results of this study show that this approach can be used to predict metamorphic relations for matrix calculation programs as well.« less
  6. 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 »metamorphic testing. This paper provides a comprehensive evaluation on the impact of source test case generation techniques on the fault finding effectiveness of metamorphic testing. We evaluated the effectiveness of line coverage, branch coverage, weak mutation and random test generation strategies for source test case generation. The experiments are conducted with 77 methods from 4 open source code repositories. Our results show that by systematically creating source test cases, we can significantly increase the fault finding effectiveness of metamorphic testing. Further, in this paper we introduce a simple metamorphic testing tool called "METtester" that we use to conduct metamorphic testing on these methods.« less