Search

Metamorphic testing is a technique that makes use of some necessary properties of the software under test, termed as metamorphic relations, to construct new test cases, namely follow-up test cases, based on some existing test cases, namely source test cases. Due to the ability of verifying testing results without the need of test oracles, it has been widely used in many application domains and detected lots of real-life faults. Numerous investigations have been conducted to further improve the effectiveness of metamorphic testing, most of which were focused on the identification and selection of “good” metamorphic relations. Recently, a few studies emerged on the research direction of how to generate and select source test cases that are effective in fault detection. In this paper, we propose a novel approach to generating source test cases based on their associated path constraints, which are obtained through symbolic execution. The path distance among test cases is leveraged to guide the prioritization of source test cases, which further improve the efficiency. A tool has been developed to automate the proposed approach as much as possible. Empirical studies have also been conducted to evaluate the fault-detection effectiveness of the approach. The results show that this approach enhances both the performance and automation of metamorphic testing. It also highlights interesting research directions for further improving metamorphic testing.

The success of learning techniques in solving a variety of hard AI problems promotes the flourish of recognition-based applications. Many state-of-the-art text localization systems, which can detect and report the positions of text segments in an image, are mainly implemented with learning-based techniques. Data-driven learning raises a series of questions on how to verify, validate and evaluate such learning-based systems. In this paper, we propose a methodology to automatically evaluate the stability of text localization systems via metamorphic relations, where a stable system should output consistent results for similar inputs with the same text segments. We introduce six metamorphic relations that should be preserved in a stable text localization system and define the corresponding metrics for stability evaluation. With the defined metamorphic relations, we apply metamorphic testing techniques to compare the inputs and outputs to evaluate system stability, and further diagnose the causes of inconsistency. The extensive experimentation on both academic and commercial text localization systems demonstrates the effectiveness of our method on stability evaluation for such systems.

The quality assurance for machine learning systems is becoming increasingly critical nowadays. While many efforts have been paid on trained models from such systems, we focus on the quality of these systems themselves, as the latter essentially decides the quality of numerous models thus trained. In this article, we focus particularly on detecting bugs in implementing one class of model-training systems, namely, linear classification algorithms, which are known to be challenging due to the lack of test oracle. Existing work has attempted to use metamorphic testing to alleviate the oracle problem, but fallen short on overlooking the statistical nature of such learning algorithms, leading to premature metamorphic relations (MRs) suffering efficacy and necessity issues. To address this problem, we first derive MRs from a fundamental property of linear classification algorithms, i.e., algorithm stability, with the soundness guarantee. We then formulate such MRs in a way that is rare in usage but could be more effective according to our field study and analysis, i.e., Past-execution Dependent MR (PD-MR), as contrast to the traditional way, i.e., Past-execution Independent MR (PI-MR), which has been extensively studied. We experimentally evaluated our new MRs upon nine well-known linear classification algorithms. The results reported that the new MRs detected 37.6–329.2% more bugs than existing benchmark MRs.

Test suite based automated program repair (APR) relies on a test oracle to determine the execution result of individual test cases. The applicability of APR techniques, therefore, is limited by the fact that test oracles may not exist. Metamorphic Testing (MT) is a testing approach that, rather than checking the correctness of individual test outputs, checks testing results through verification of relations among multiple test cases and their outputs: MT can therefore be applied without test oracles. This paper presents an integration of MT with APR that enables application of APR without the need for a test oracle. Two important issues for this integration which have been thoroughly investigated and addressed are: (1) feasibility — which is addressed by proposing a framework to support the integration, and then presenting MT-GenProg, a tool incorporating MT with the popular APR technique GenProg; and (2) effectiveness — which is confirmed through an empirical study of GenProg and MT-GenProg on 1,143 program versions from the IntroClass benchmark suite, demonstrating MT-GenProg’s comparable performance to GenProg, in terms of repair effectiveness. We conclude that the proposed integration is both practically feasible and effective, and thus successfully extends APR techniques to a broader application domain.

Metamorphic testing is a promising technique for testing software systems when the oracle problem exists, and has been successfully applied to various application domains and paradigms. An important and essential task in metamorphic testing is the identification of metamorphic relations, which, due to the absence of a systematic and specification-based methodology, has often been done in an ad hoc manner—something which has hindered the applicability and effectiveness of metamorphic testing. To address this, a systematic methodology for identifying metamorphic relations based on the category-choice framework, called metric , is introduced in this paper. A tool implementing this methodology has been developed and examined in an experiment to determine the viability and effectiveness of metric , with the results of the experiment confirming that metric is both effective and efficient at identifying metamorphic relations.

Metamorphic Testing is a software testing paradigm which aims at using necessary properties of a system under test, called metamorphic relations, to either check its expected outputs, or to generate new test cases. Metamorphic Testing has been successful to test programs for which a full oracle is not available or to test programs for which there are uncertainties on expected outputs such as learning systems. In this article, we propose Adaptive Metamorphic Testing as a generalization of a simple yet powerful reinforcement learning technique, namely contextual bandits, to select one of the multiple metamorphic relations available for a program. By using contextual bandits, Adaptive Metamorphic Testing learns which metamorphic relations are likely to transform a source test case, such that it has higher chance to discover faults. We present experimental results over two major case studies in machine learning, namely image classification and object detection, and identify weaknesses and robustness boundaries. Adaptive Metamorphic Testing efficiently identifies weaknesses of the tested systems in context of the source test case.