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1. Using semi-supervised learning for predicting metamorphic relations

   https://doi.org/10.1145/3193977.3193985  

   Hardin, Bonnie; Kanewala, Upulee (January 2018, The 3rd International Workshop on Metamorphic Testing)

   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 with a supervised model, and show that the addition of unlabeled data improves the classification accuracy of the MR prediction model. 

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2. Metamorphic Runtime Checking of Applications Without Test Oracles

   Bell, Jonathan; Murphy, Christian; Kaiser, Gail (March 2015, Crosstalk)

   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. 

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3. Dynamic Inference of Likely Metamorphic Properties to Support Differential Testing

   https://doi.org/10.1109/AST.2015.19  

   Su, Fang-Hsiang; Bell, Jonathan; Murphy, Christian; Kaiser, Gail (May 2015, 10th IEEE/ACM International Workshop on Automation of Software Test (AST))

   Metamorphic testing is an advanced technique to test programs without a true test oracle such as machine learning applications. Because these programs have no general oracle to identify their correctness, traditional testing techniques such as unit testing may not be helpful for developers to detect potential bugs. This paper presents a novel system, KABU, which can dynamically infer properties of methods' states in programs that describe the characteristics of a method before and after transforming its input. These Metamorphic Properties (MPs) are pivotal to detecting potential bugs in programs without test oracles, but most previous work relies solely on human effort to identify them and only considers MPs between input parameters and output result (return value) of a program or method. This paper also proposes a testing concept, Metamorphic Differential Testing (MDT). By detecting different sets of MPs between different versions for the same method, KABU reports potential bugs for human review. We have performed a preliminary evaluation of KABU by comparing the MPs detected by humans with the MPs detected by KABU. Our preliminary results are promising: KABU can find more MPs than human developers, and MDT is effective at detecting function changes in methods. 

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4. Fault Detection Effectiveness of Metamorphic Relations Developed for Testing Supervised Classifiers

   https://doi.org/10.1109/AITest.2019.00019  

   Saha, Prashanta; Kanewala, Upulee (April 2019, 2019 IEEE International Conference On Artificial Intelligence Testing (AITest))

   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 verify 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. 

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5. ConPredictor: Concurrency Defect Prediction in Real-World Applications

   https://doi.org/10.1109/TSE.2018.2791521  

   Yu, Tingting; Wen, Wei; Han, Xue; Hayes, Jane (January 2018, IEEE Transactions on Software Engineering)

   Concurrent programs are difficult to test due to their inherent non-determinism. To address this problem, testing often requires the exploration of thread schedules of a program; this can be time-consuming when applied to real-world programs. Software defect prediction has been used to help developers find faults and prioritize their testing efforts. Prior studies have used machine learning to build such predicting models based on designed features that encode the characteristics of programs. However, research has focused on sequential programs; to date, no work has considered defect prediction for concurrent programs, with program characteristics distinguished from sequential programs. In this paper, we present ConPredictor, an approach to predict defects specific to concurrent programs by combining both static and dynamic program metrics. Specifically, we propose a set of novel static code metrics based on the unique properties of concurrent programs. We also leverage additional guidance from dynamic metrics constructed based on mutation analysis. Our evaluation on four large open source projects shows that ConPredictor improved both within-project defect prediction and cross-project defect prediction compared to traditional features. 

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