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Test Case Prioritization Using Firefly Algorithm for software testing

IEEE ACCESS 2019年 7卷 132360-132373页

作者： Khatibsyarbini, Muhammad Isa, Mohd Adham Jawawi, Dayang N. A. Hamed, Haza Nuzly Abdull Suffian, Muhammad Dhiauddin Mohamed Univ Teknol Malaysia Fac Engn Sch Comp Johor Baharu 81310 Malaysia MIMOS Technol Solut Sdn Bhd Business Solut & Serv Kuala Lumpur 57000 Malaysia

software testing is a vital and complex part of the software development life cycle. Optimization of software testing is still a major challenge, as prioritization of test cases remains unsatisfactory in terms of Average Percentage of Faults Detected (APFD) and time execution performance. This is attributed to a large search space to find an optimal ordering of test cases. In this paper, we have proposed an approach to prioritize test cases optimally using Firefly Algorithm. To optimize the ordering of test cases, we applied Firefly Algorithm with fitness function defined using a similarity distance model. Experiments were carried on three benchmark programs with test suites extracted from software-artifact Infrastructure Repository (SIR). Our Test Case Prioritization (TCP) technique using Firefly Algorithm with similarity distance model demonstrated better if not equal in terms of APFD and time execution performance compared to existing works. Overall APFD results indicate that Firefly Algorithm is a promising competitor in TCP applications.

关键词： Firefly Algorithm metaheuristic software engineering artificial intelligence search-based software testing test case prioritization

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Fault-driven stress testing of distributed real-time software based on UML models

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software testing VERIFICATION & RELIABILITY 2011年第2期21卷 101-124页

作者： Garousi, Vahid Univ Calgary Dept Elect & Comp Engn Software Qual Engn Res Grp SoftQual Schulich Sch Engn Calgary AB T2N 1N4 Canada

In a previous article, a stress testing methodology was reported to detect network traffic-related Real-Time (RT) faults in distributed RT systems based on the design UML model of a System Under Test (SUT). The stress methodology, referred to as Test LOcation-driven Stress testing (TLOST), aimed at increasing the chances of RT failures (violations in RT constraints) associated with a given stress test location (an network or a node under test). As demonstrated and experimented in this article, although TLOST is useful in stress testing different test locations (nodes and network, it does not guarantee to target (test) all RT constraints in an SUT. This is because the durations of message sequences bounded by some RT constraints might never be exercised (covered) by TLOST. A complementary stress test methodology is proposed in this article, which guarantees to target (cover) all RT constraints in an SUT and detect their potential RT faults (if any). Using a case study, this article shows that the new complementary methodology is capable of targeting the RT faults not detected by the previous test methodology. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： search-based software testing stress testing model-based testing fault-driven testing distributed systems UML network traffic

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An Empirical Study on Automated Test Generation Tools for Java:Effectiveness and Challenges

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Journal of Computer Science & Technology 2024年第3期39卷 715-736页

作者：刘相君余萍马晓星 State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China CCF ACM IEEE

Automated test generation tools enable test automation and further alleviate the low efficiency caused by writing hand-crafted test ***,existing automated tools are not mature enough to be widely used by software testing *** paper conducts an empirical study on the state-of-the-art automated tools for Java,i.e.,EvoSuite,Randoop,JDoop,JTeXpert,T3,and *** design a test workflow to facilitate the process,which can automatically run tools for test generation,collect data,and evaluate various ***,we conduct empirical analysis on these six tools and their related techniques from different aspects,i.e.,code coverage,mutation score,test suite size,readability,and real fault detection *** discuss about the benefits and drawbacks of hybrid techniques based on experimental ***,we introduce our experience in setting up and executing these tools,and summarize their usability and ***,we give some insights into automated tools in terms of test suite readability improvement,meaningful assertion generation,test suite reduction for random testing tools,and symbolic execution integration.

关键词： automated test generation search-based software testing random testing symbolic execution

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Basic block coverage for search-based unit testing and crash reproduction

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EMPIRICAL software ENGINEERING 2022年第7期27卷 1-41页

作者： Derakhshanfar, Pouria Devroey, Xavier Zaidman, Andy Delft Univ Technol Postbus 5 NL-2600 AA Delft Netherlands Univ Namur Namur Digital Inst Rue Bruxelles 61 B-5000 Namur Belgium

search-based techniques have been widely used for white-box test generation. Many of these approaches rely on the approach level and branch distance heuristics to guide the search process and generate test cases with high line and branch coverage. Despite the positive results achieved by these two heuristics, they only use the information related to the coverage of explicit branches (e.g., indicated by conditional and loop statements), but ignore potential implicit branchings within basic blocks of code. If such implicit branching happens at runtime (e.g., if an exception is thrown in a branchless-method), the existing fitness functions cannot guide the search process. To address this issue, we introduce a new secondary objective, called Basic Block Coverage (BBC), which takes into account the coverage level of relevant basic blocks in the control flow graph. We evaluated the impact of BBC on search-based unit test generation (using the DvsTAMOSA algorithm) and search-based crash reproduction (using the STDistance and WeightedSum fitness functions). Our results show that for unit test generation, BBC improves the branch coverage of the generated tests. Although small (similar to 1.5%), this improvement in the branch coverage is systematic and leads to an increase of the output domain coverage and implicit runtime exception coverage, and of the diversity of runtime states. In terms of crash reproduction, in the combination of STDistance and WeightedSum, BBC helps in reproducing 3 new crashes for each fitness function. BBC significantly decreases the time required to reproduce 43.5% and 45.1% of the crashes using STDistance and WeightedSum, respectively. For these crashes, BBC reduces the consumed time by 71.7% (for STDistance) and 68.7% (for WeightedSum) on average.

关键词： Automated crash reproduction search-based software testing Evolutionary algorithm Secondary objective

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A benchmark-based evaluation of search-based crash reproduction

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EMPIRICAL software ENGINEERING 2020年第1期25卷 96-138页

作者： Soltani, Mozhan Derakhshanfar, Pouria Devroey, Xavier Van Deursen, Arie Delft Univ Technol Postbus 5 Delft AA Netherlands Leiden Univ Rapenburg 70 NL-2311 EZ Leiden Netherlands

Crash reproduction approaches help developers during debugging by generating a test case that reproduces a given crash. Several solutions have been proposed to automate this task. However, the proposed solutions have been evaluated on a limited number of projects, making comparison difficult. In this paper, we enhance this line of research by proposing JCrashPack, an extensible benchmark for Java crash reproduction, together with ExRunner, a tool to simply and systematically run evaluations. JCrashPack contains 200 stack traces from various Java projects, including industrial open source ones, on which we run an extensive evaluation of EvoCrash, the state-of-the-art tool for search-based crash reproduction. EvoCrash successfully reproduced 43% of the crashes. Furthermore, we observed that reproducing NullPointerException, IllegalArgumentException, and IllegalStateException is relatively easier than reproducing ClassCastException, ArrayIndexOutOfBoundsException and StringIndexOutOfBoundsException. Our results include a detailed manual analysis of EvoCrash outputs, from which we derive 14 current challenges for crash reproduction, among which the generation of input data and the handling of abstract and anonymous classes are the most frequents. Finally, based on those challenges, we discuss future research directions for search-based crash reproduction for Java.

关键词： Crash reproduction Empirical evaluation search-based software testing

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search-based Crash Reproduction and Its Impact on Debugging

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IEEE TRANSACTIONS ON software ENGINEERING 2020年第12期46卷 1294-1317页

作者： Soltani, Mozhan Panichella, Annibale van Deursen, Arie Delft Univ Technol Software Engn Res Grp NL-2628 Delft Netherlands

software systems fail. These failures are often reported to issue tracking systems, where they are prioritized and assigned to responsible developers to be investigated. When developers debug software, they need to reproduce the reported failure in order to verify whether their fix actually prevents the failure from happening again. Since manually reproducing each failure could be a complex task, several automated techniques have been proposed to tackle this problem. Despite showing advancements in this area, the proposed techniques showed various types of limitations. In this paper, we present EvoCrash, a new approach to automated crash reproduction based on a novel evolutionary algorithm, called Guided Genetic Algorithm (GGA). We report on our empirical study on using EvoCrash to reproduce 54 real-world crashes, as well as the results of a controlled experiment, involving human participants, to assess the impact of EvoCrash tests in debugging. based on our results, EvoCrash outperforms state-of-the-art techniques in crash reproduction and uncovers failures that are undetected by classical coverage-based unit test generation tools. In addition, we observed that using EvoCrash helps developers provide fixes more often and take less time when debugging, compared to developers debugging and fixing code without using EvoCrash tests.

关键词： Debugging Tools software Core dumps Explosions Computer bugs search-based software testing genetic algorithms automated crash reproduction empirical software engineering

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Input Domain Reduction through Irrelevant Variable Removal and Its Effect on Local, Global, and Hybrid search-based Structural Test Data Generation

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IEEE TRANSACTIONS ON software ENGINEERING 2012年第2期38卷 453-477页

作者： McMinn, Phil Harman, Mark Lakhotia, Kiran Hassoun, Youssef Wegener, Joachim Univ Sheffield Sheffield S1 4DP S Yorkshire England UCL CREST London WC1E 6BT England Kings Coll London London WC2R 2LS England Berner & Mattner Syst Tech GmbH D-10587 Berlin Germany

search-based Test Data Generation reformulates testing goals as fitness functions so that test input generation can be automated by some chosen search-based optimization algorithm. The optimization algorithm searches the space of potential inputs, seeking those that are "fit for purpose," guided by the fitness function. The search space of potential inputs can be very large, even for very small systems under test. Its size is, of course, a key determining factor affecting the performance of any search-based approach. However, despite the large volume of work on search-based software testing, the literature contains little that concerns the performance impact of search space reduction. This paper proposes a static dependence analysis derived from program slicing that can be used to support search space reduction. The paper presents both a theoretical and empirical analysis of the application of this approach to open source and industrial production code. The results provide evidence to support the claim that input domain reduction has a significant effect on the performance of local, global, and hybrid search, while a purely random search is unaffected.

关键词： search-based software testing evolutionary testing automated test data generation input domain reduction

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On the Faults Found in REST APIs by Automated Test Generation

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ACM TRANSACTIONS ON software ENGINEERING AND METHODOLOGY 2022年第3期31卷 1–43页

作者： Marculescu, Bogdan Zhang, Man Arcuri, Andrea Kristiania Univ Coll PB 1190 N-0107 Oslo Norway Oslo Metropolitan Univ PB 1190 N-0107 Oslo Norway

RESTful web services are often used for building a wide variety of enterprise applications. The diversity and increased number of applications using RESTful APIs means that increasing amounts of resources are spent developing and testing these systems. Automation in test data generation provides a useful way of generating test data in a fast and efficient manner. However, automated test generation often results in large test suites that are hard to evaluate and investigate manually. This article proposes a taxonomy of the faults we have found using search-based software testing techniques applied on RESTful APIs. The taxonomy is a first step in understanding, analyzing, and ultimately fixing software faults in web services and enterprise applications. We propose to apply a density-based clustering algorithm to the test cases evolved during the search to allow a better separation between different groups of faults. This is needed to enable engineers to highlight and focus on the most serious faults. Tests were automatically generated for a set of eight case studies, seven open-source and one industrial. The test cases generated during the search are clustered based on the reported last executed line and based on the error messages returned, when such error messages were available. The tests were manually evaluated to determine their root causes and to obtain additional information. The article presents a taxonomy of the faults found based on the manual analysis of 415 faults in the eight case studies and proposes a method to support the classification using clustering of the resulting test cases.

关键词： search-based software testing automated software testing automated Test Generation evolutionary Algorithms

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An orchestrated survey of methodologies for automated software test case generation

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JOURNAL OF SYSTEMS AND software 2013年第8期86卷 1978-2001页

作者： Anand, Saswat Burke, Edmund K. Chen, Tsong Yueh Clark, John Cohen, Myra B. Grieskamp, Wolfgang Harman, Mark Harrold, Mary Jean McMinn, Phil Bertolino, Antonia Li, J. Jenny Zhu, Hong Stanford Univ Dept Comp Sci Stanford CA 94305 USA Univ Stirling Stirling FK9 4LA Scotland Swinburne Univ Technol Hawthorn Vic 3122 Australia Univ York York YO10 5DD N Yorkshire England Univ Nebraska Lincoln Dept Comp Sci & Engn Lincoln NE USA Microsoft Res Redmond WA USA UCL Dept Comp Sci London WC1E 6BT England Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA Univ Sheffield Sheffield S10 2TN S Yorkshire England CNR ISTI I-00185 Rome Italy Avaya Labs Res Basking Ridge NJ USA Oxford Brookes Univ Oxford OX3 0BP England

Test case generation is among the most labour-intensive tasks in software testing. It also has a strong impact on the effectiveness and efficiency of software testing. For these reasons, it has been one of the most active research topics in software testing for several decades, resulting in many different approaches and tools. This paper presents an orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections. The techniques presented include: (a) structural testing using symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its variant of adaptive random testing, and (e) search-based testing. Each section is contributed by world-renowned active researchers on the technique, and briefly covers the basic ideas underlying the method, the current state of the art, a discussion of the open research problems, and a perspective of the future development of the approach. As a whole, the paper aims at giving an introductory, up-to-date and (relatively) short overview of research in automatic test case generation, while ensuring a comprehensive and authoritative treatment. (c) 2013 Elsevier Inc. All rights reserved.

关键词： Adaptive random testing Combinatorial testing Model-based testing Orchestrated survey search-based software testing software testing Symbolic execution Test automation Test case generation

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Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key Features

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ACM TRANSACTIONS ON software ENGINEERING AND METHODOLOGY 2024年第4期33卷 1-32页

作者： Neelofar, Neelofar Aleti, Aldeida Monash Univ Melbourne Australia

Ensuring the safety of autonomous vehicles (AVs) is of utmost importance, and testing them in simulated environments is a safer option than conducting in-field operational tests. However, generating an exhaustive test suite to identify critical test scenarios is computationally expensive, as the representation of each test is complex and contains various dynamic and static features, such as the AV under test, road participants (vehicles, pedestrians, and static obstacles), environmental factors (weather and light), and the road's structural features (lanes, turns, road speed, etc.). In this article, we present a systematic technique that uses Instance Space Analysis (ISA) to identify the significant features of test scenarios that affect their ability to reveal the unsafe behaviour of AVs. ISA identifies the features that best differentiate safety-critical scenarios from normal driving and visualises the impact of these features on test scenario outcomes (safe/unsafe) in two dimensions. This visualisation helps to identify untested regions of the instance space and provides an indicator of the quality of the test suite in terms of the percentage of feature space covered by testing. To test the predictive ability of the identified features, we train five Machine Learning classifiers to classify test scenarios as safe or unsafe. The high precision, recall, and F1 scores indicate that our proposed approach is effective in predicting the outcome of a test scenario without executing it and can be used for test generation, selection, and prioritisation.

关键词： testing autonomous vehicles feature-impact analysis instance space analysis search-based software testing

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