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تسجيل مستخدم جديدUsing mutation testing to measure behavioural test diversity
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نشر من قبل
Francisco de Oliveira Neto
تاريخ النشر
2020
مجال البحث
الهندسة المعلوماتية
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English
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Diversity has been proposed as a key criterion to improve testing effectiveness and efficiency.It can be used to optimise large test repositories but also to visualise test maintenance issues and raise practitioners awareness about waste in test artefacts and processes. Even though these diversity-based testing techniques aim to exercise diverse behavior in the system under test (SUT), the diversity has mainly been measured on and between artefacts (e.g., inputs, outputs or test scripts). Here, we introduce a family of measures to capture behavioural diversity (b-div) of test cases by comparing their executions and failure outcomes. Using failure information to capture the SUT behaviour has been shown to improve effectiveness of history-based test prioritisation approaches. However, history-based techniques require reliable test execution logs which are often not available or can be difficult to obtain due to flaky tests, scarcity of test executions, etc. To be generally applicable we instead propose to use mutation testing to measure behavioral diversity by running the set of test cases on various mutat
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Diversity has been used as an effective criteria to optimise test suites for cost-effective testing. Particularly, diversity-based (alternatively referred to as similarity-based) techniques have the benefit of being generic and applicable across diff
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Deep learning (DL) defines a new data-driven programming paradigm where the internal system logic is largely shaped by the training data. The standard way of evaluating DL models is to examine their performance on a test dataset. The quality of the t
est dataset is of great importance to gain confidence of the trained models. Using an inadequate test dataset, DL models that have achieved high test accuracy may still lack generality and robustness. In traditional software testing, mutation testing is a well-established technique for quality evaluation of test suites, which analyzes to what extent a test suite detects the injected faults. However, due to the fundamental difference between traditional software and deep learning-based software, traditional mutation testing techniques cannot be directly applied to DL systems. In this paper, we propose a mutation testing framework specialized for DL systems to measure the quality of test data. To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i.e., training data and training programs). Then we design a set of model-level mutation operators that directly inject faults into DL models without a training process. Eventually, the quality of test data could be evaluated from the analysis on to what extent the injected faults could be detected. The usefulness of the proposed mutation testing techniques is demonstrated on two public datasets, namely MNIST and CIFAR-10, with three DL models.
Deep Learning (DL) components are routinely integrated into software systems that need to perform complex tasks such as image or natural language processing. The adequacy of the test data used to test such systems can be assessed by their ability to
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