5,376 research outputs found

    Testing scientific software: techniques for automatic detection of metamorphic relations

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    2015 Spring.Includes bibliographical references.Scientific software plays an important role in critical decision making in fields such as the nuclear industry, medicine, and the military. Systematic testing of such software can help to ensure that it works as expected. Comprehensive, automated software testing requires an oracle to check whether the output produced by a test case matches the expected behavior of the program. But the challenges in creating suitable oracles limit the ability to perform automated testing of scientific software. For some programs, creating an oracle may be not possible since the correct output is not known a priori. Further, it may be impractical to implement an oracle for an arbitrary input due to the complexity of a program. The software testing community refers to such programs as non-testable. Many scientific programs fall into this category of non-testable programs, since they are either written to find answers that are previously unknown or they perform complex calculations. In this work, we developed techniques to automatically predict metamorphic relations by analyzing the program structure. These metamorphic relations can serve as automated partial test oracles in scientific software. Metamorphic testing is a method for automating the testing process for programs without test oracles. This technique operates by checking whether a program behaves according to a certain set of properties called metamorphic relations. A metamorphic relation is a relationship between multiple input and output pairs of the program. It specifies how the output should change following a specific change made to the input. A change in the output that differs from what is specified by the metamorphic relation indicates a fault in the program. Metamorphic testing can be effective in testing machine learning applications, bioinformatics programs, health-care simulations, partial differential equations and other programs. Unfortunately, finding appropriate metamorphic relations for use in metamorphic testing remains a labor intensive task that is generally performed by a domain expert or a programmer. In this work we applied novel machine learning based approaches to automatically derive metamorphic relations. We first evaluated the effectiveness of modeling the metamorphic relation prediction problem as a binary classification problem. We found that support vector machines are the most effective binary classifiers for predicting metamorphic relations. We also found that using walk-based graph kernels for feature extraction from graph-based program representations further improves the prediction accuracy. In addition, incorporating mathematical properties of operations in the graph kernel computation improves the prediction accuracy. Further, we found that control flow information of a function are more effective than data dependency information for predicting metamorphic relations. Finally we investigated the possibility of creating multi-label classifiers that can predict multiple metamorphic relations using a single classifier. Our empirical studies show that multi-label classifiers are not effective as binary classifiers for predicting metamorphic relations. Automated testing will make the testing process faster, reduce the testing cost and make it more reliable. Automated testing requires automated test oracles. Automatically discovering metamorphic relations is an important step towards automating oracle creation. Work presented here is the first attempt towards developing automated techniques for deriving metamorphic relations. Our work contributes toward automating the testing process of programs that face oracle problems

    Fault Detection Effectiveness of Metamorphic Relations Developed for Testing Supervised Classifiers

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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 \emph{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.Comment: 8 pages, AITesting 201

    Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing

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    We have recently witnessed tremendous success of Machine Learning (ML) in practical applications. Computer vision, speech recognition and language translation have all seen a near human level performance. We expect, in the near future, most business applications will have some form of ML. However, testing such applications is extremely challenging and would be very expensive if we follow today's methodologies. In this work, we present an articulation of the challenges in testing ML based applications. We then present our solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers. We have developed metamorphic relations for an application based on Support Vector Machine and a Deep Learning based application. Empirical validation showed that our approach was able to catch 71% of the implementation bugs in the ML applications.Comment: Published at 27th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2018
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