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35th Annual IEEE International Computer Software and Applications Conference (COMPSAC)

作者： Merkel, Robert Kuo, Fei-Ching Chen, Tsong Yueh Monash Univ Fac Informat Technol Clayton Vic 3800 Australia Swinburne Univ Technol Ctr Software Anal & Testing Hawthorn Vic 3122 Australia

ISBN: (纸本)9780769544397

In random testing, the distribution of the generated test cases is known as the test profile. We consider the effects of different test profiles, taking advantage of probabilistic information about likely failure-revealing inputs, on the effectiveness of random testing for debugging. We examine a failure-proportional testing strategy, in which tests are randomly sampled with replacement, with probability proportional to a previously identified failure probability distribution, compared to a uniform testing strategy, in which tests are randomly sampled uniformly from the entire input domain. We show that neither strategy optimises failure-detection capabilities, and show an alternative strategy that does. We also consider selection without replacement, and examine the robustness of some strategies given a divergence between the estimated and actual failure probability distributions.

关键词： testing test profiles failure-based testing random testing adaptive random testing

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Code Coverage of Adaptive Random testing

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IEEE TRANSACTIONS ON RELIABILITY 2013年第1期62卷 226-237页

作者： Chen, Tsong Yueh Kuo, Fei-Ching Liu, Huai Wong, W. Eric Swinburne Univ Technol Fac Informat & Commun Technol Hawthorn Vic 3122 Australia Univ Texas Dallas Dept Comp Sci Richardson TX 75083 USA

Random testing is a basic software testing technique that can be used to assess the software reliability as well as to detect software failures. Adaptive random testing has been proposed to enhance the failure-detection capability of random testing. Previous studies have shown that adaptive random testing can use fewer test cases than random testing to detect the first software failure. In this paper, we evaluate and compare the performance of adaptive random testing and random testing from another perspective, that of code coverage. As shown in various investigations, a higher code coverage not only brings a higher failure-detection capability, but also improves the effectiveness of software reliability estimation. We conduct a series of experiments based on two categories of code coverage criteria: structure-based coverage, and fault-based coverage. Adaptive random testing can achieve higher code coverage than random testing with the same number of test cases. Our experimental results imply that, in addition to having a better failure-detection capability than random testing, adaptive random testing also delivers a higher effectiveness in assessing software reliability, and a higher confidence in the reliability of the software under test even when no failure is detected.

关键词： Adaptive random testing code coverage failure-based testing random testing

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Adaptive Random testing: The ART of test case diversity

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JOURNAL OF SYSTEMS AND SOFTWARE 2010年第1期83卷 60-66页

作者： Chen, Tsong Yueh Kuo, Fei-Ching Merkel, Robert G. Tse, T. H. Swinburne Univ Technol Fac Informat & Commun Technol Hawthorn Vic 3122 Australia Univ Hong Kong Dept Comp Sci Pokfulam Hong Kong Peoples R China

Random testing is not only a useful testing technique in itself, but also plays a core role in many other testing methods. Hence, any significant improvement to random testing has an impact throughout the software testing community. Recently, Adaptive Random testing (ART) was proposed as an effective alternative to random testing. This paper presents a synthesis of the most important research results related to ART. In the course of our research and through further reflection, we have realised how the techniques and concepts of ART can be applied in a much broader context, which we present here. We believe such ideas can be applied in a variety of areas of software testing, and even beyond software testing. Amongst these ideas, we particularly note the fundamental role of diversity in test case selection strategies. We hope this paper serves to provoke further discussions and investigations of these ideas. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Software testing Random testing Adaptive random testing Adaptive random sequence failure-based testing failure pattern

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Does Adaptive Random testing Deliver a Higher Confidence than Random testing?

Does Adaptive Random Testing Deliver a Higher Confidence tha...

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8th International Conference on Quality Software

作者： Chen, Tsong Yueh Kuo, Fei-Ching Liu, Huai Wong, W. Eric Swinburne Univ Technol Fac Informat & Commun Technol Hawthorn Vic 3122 Australia Univ Texas Dallas Dept Comp Sci Richardson TX USA

ISBN: (纸本)9780769533124

Random testing (RT) is a fundamental software testing technique. Motivated by the rationale that neighbouring test cases tend to cause similar execution behaviours, adaptive random testing (ART) was proposed as an enhancement of RT which enforces random test cases evenly spread over the input domain. ART has always been compared with RT from the perspective of the failure-detection capability. Previous studies have shown that ART can use fewer test cases to detect the first software failure than RT In this paper we aim to compare ART and RT from the perspective of program-based coverage. Our experimental results show that given the same number of test cases, ART normally has a higher percentage of coverage than RT In conclusion, ART outperforms RT not only in terms of the failure detection capability, but also in terms of the thoroughness of program-based coverage. Therefore, ART delivers a higher confidence of the software under test than RT even when no failure has been revealed.

关键词： failure-based testing Random testing Adaptive Random testing Test Coverage Criteria

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Identification of failure Regions for Programs With Numeric Inputs

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021年第4期5卷 651-667页

作者： Huang, Rubing Sun, Weifeng Chen, Tsong Yueh Ng, Sebastian Chen, Jinfu Jiangsu Univ Sch Comp Sci & Commun Engn Jiangsu Key Lab Secur Technol Ind Cyberspace Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Hawthorn Vic 3122 Australia

failure region, where failure-causing inputs reside, has provided many insights to enhance testing effectiveness of many testing methods. failure region may also provide some important information to support other processes such as software debugging. When a testing method detects a software failure, indicating that a failure-causing input is identified, the next important question is about how to identify the failure region based on this failure-causing input, i.e., Identification of failure Regions (IFR). In this paper, we introduce a new IFR strategy, namely Search for Boundary (SB), to identify an approximate failure region of a numeric input domain. SB attempts to identify additional failure-causing inputs that are as close to the boundary of the failure region as possible. To support SB, we provide a basic procedure, and then propose two methods, namely Fixed-orientation Search for Boundary (FSB) and Diverse-orientation Search for Boundary (DSB). In addition, we implemented an automated experimentation platform to integrate these methods. In the experiments, we evaluated the proposed SB methods using a series of simulation studies and empirical studies with different types of failure regions. The results show that our methods can effectively identify a failure region, within the limited testing resources.

关键词： Shape Software Flyback transformers Software testing Subspace constraints Sun Software debugging software testing failure-based testing identification of failure region (IFR)

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Effective Test Suites for Mixed Discrete-Continuous Stateflow Controllers 2015

Effective Test Suites for Mixed Discrete-Continuous Stateflo...

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10th Joint Meeting of the European Software Engineering Conference (ESEC) / ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE)

作者： Matinnejad, Reza Nejati, Shiva Briand, Lionel C. Bruckmann, Thomas Univ Luxembourg SnT Ctr Luxembourg Luxembourg Delphi Automot Syst Luxembourg Luxembourg

ISBN: (纸本)9781450336758

Modeling mixed discrete-continuous controllers using Stateflow is common practice and has a long tradition in the embedded software system industry. testing Stateflow models is complicated by expensive and manual test oracles that are not amenable to full automation due to the complex continuous behaviors of such models. In this paper, we reduce the cost of manual test oracles by providing test case selection algorithms that help engineers develop small test suites with high fault revealing power for Stateflow models. We present six test selection algorithms for discrete-continuous State-flows: An adaptive random test selection algorithm that diversifies test inputs, two white-box coverage-based algorithms, a black-box algorithm that diversifies test outputs, and two search-based black-box algorithms that aim to maximize the likelihood of presence of continuous output failure patterns. We evaluate and compare our test selection algorithms, and find that our three output-based algorithms consistently outperform the coverage-and input-based algorithms in revealing faults in discrete-continuous Stateflow models. Further, we show that our output-based algorithms are complementary as the two search-based algorithms perform best in revealing specific failures with small test suites, while the output diversity algorithm is able to identify different failure types better than other algorithms when test suites are above a certain size.

关键词： Stateflow testing mixed discrete-continuous behaviors structural coverage failure-based testing output diversity

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Adaptive Random testing in Detecting Layout Faults of Web Applications

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2018年第10期28卷 1399-1428页

作者： Selay, Elmin Zhou, Zhi Quan Chen, Tsong Yueh Kuo, Fei-Ching Univ Wollongong Sch Comp & Informat Technol Inst Cybersecur & Cryptol Wollongong NSW 2522 Australia Swinburne Univ Technol Dept Comp Sci & Software Engn Hawthorn Vic 3122 Australia

As part of a software testing process, output verification poses a challenge when the output is not numeric or textual, such as graphical. The industry practice of using human oracles (testers) to observe and verify the correctness of the actual results is both expensive and error-prone. In particular, this practice is usually unsustainable when developing web applications - the most popular software of our era. This is because web applications change frequently due to the fast-evolving requirements amid popular demand. To improve the cost effectiveness of browser output verification, in this study we design failure-based testing techniques and evaluate the effectiveness and efficiency thereof in the context of web testing. With a novel application of the concept of adaptive random sequence (ARS), our approach leverages peculiar characteristics of failure patterns found in browser layout rendering. An empirical study shows that the use of failure patterns and inclination to guide the testing flow leads to more cost-effective results than other classic methods. This study extends the application of ARSs from the input space of programs to their output space, and also shows that adaptive random testing (ART) can outperform random testing (RT) in both failure detection effectiveness (in terms of F-measure) and failure detection efficiency (in terms of execution time).

关键词： Adaptive random testing failure-based testing web testing adaptive random sequence graphical output verification layout fault failure pattern

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A Two-Stage Algorithm for Identifying Software failure Regions

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IEEE TRANSACTIONS ON RELIABILITY 2024年

作者： Mao, Chengying Zhu, Zheng Chen, Tsong Yueh Towey, Dave Wen, Linlin Chen, Jifu Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330013 Peoples R China Zhejiang Univ Sch Software Technol Ningbo 315048 Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Hawthorn Vic 3122 Australia Univ Nottingham Ningbo China Sch Comp Sci Ningbo 315100 Peoples R China

Software developers can only obtain a very small amount of information from the individual failure-causing inputs, which makes debugging difficult. Therefore, it is necessary to explore additional failure-causing inputs (failure regions) using the known failure-causing inputs. In order to accurately and efficiently identify the failure region, we propose a novel two-stage search algorithm, TS-FRI. In the initial exploration stage, a round-robin search identifies several boundary failure-causing points, and the failure region's centroid is estimated. During the main search stage, the boundary failure-causing points are identified through iterative division of the input domain with an equally sized partitioning strategy. This results in the boundary points being as dispersed as possible around the failure-region boundary, with the polytope formed by the points approximating the failure region (e.g., a polygon in two dimensions). The proposed algorithm is validated through simulation and empirical analysis: The experimental results show that the TS-FRI accuracy is at least comparable to the best accuracy of the compared three algorithms, and can be ten times better. In addition, TS-FRI only takes a quarter of the computation time and half the failure-validation cost of the other algorithms.

关键词： Software Software algorithms Codes Costs Accuracy Debugging Software testing Accuracy and efficiency failure region identification failure-based testing software failure software testing and debugging

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