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IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) / BigDataSE Conference / CSE Conference / EUC Conference / ISCI Conference

作者： Qin, Zhongyuan Fan, Jiarong Li, Zeru Liu, Xujian Sun, Xin Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China State Grid Zhejiang Elect Power Co Ltd Res Inst Internet Technol Ctr Hangzhou Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Fuzzing is widely used in vulnerability mining because of its simplicity and efficiency. The fuzzing tool generates numerous testcases according to the mutation of the initial seed and inputs them into the program to be tested. At the same time, it monitors exceptions of the running program to find possible software bugs. In order to improve the performance of fuzzing, many researchers are committed to generating various initial testcases. However, at present, fuzzing testcase generation does not make full use of the information of testcases, the generation process is uncontrollable, and the generation effect is general. In view of the problems mentioned above, this paper proposes a testcase generation method based on conditional generative adversarial networks. The CWGAN-GP (Conditional Wasserstein Generic Adversarial Network-Gradient Penalty) learns the format features of testcases, and generates testcases covering corresponding branches according to the input branch information. This paper conducted experiments on 6 common programs, and the experiments showed that compared to the initial training set, testcases generated by (C)WGAN-GP can extend the exploration of the program, and thus find more vulnerabilities. The CWGAN-GP model uses the branch vector in the training set as the condition to generate testcases, which can find more crashes and hangs, and improve the final fuzzing performance.

关键词： fuzzing branch coverage testcase generation one dimensional convolutions CWGAN-GP

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Carbon-Box Testing 18

Carbon-Box Testing

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18th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE)

作者： Godboley, Sangharatna Rani, G. Monika Nenavath, Sindhu Natl Inst Technol Warangal Dept Comp Sci & Engn Warangal Telangana India

ISBN: (纸本)9789897586477

Combinatorial testing tools can be used to generate test cases automatically. The existing methodologies such as Random Testing etc. have always the scope of achieving better branch coverage. This is because most of the time the boundary values which are corner cases have been ignored to consider, as a result, we achieve low branch coverage. In this paper, we present a new type of testing type named Carbon-Box Testing. This Carbon name justifies the influence of Black-Box testing techniques we use with a lightweight White-Box testing technique. We show the strength of our proposed method i.e. Dictionary Testing to enhance the branch coverage. In Dictionary Testing, we trace the input variables and their dependent values statically and use them as test inputs. This is a fact that utilizing the statically extracted values is insufficient for achieving the maximal branch coverage, hence we consider Random Testing to generate the test inputs. The initial values are the real-time Linux process ids, and then we perform mini-fuzzing with basic arithmetic operations to produce more test inputs. Pairwise testing or 2-way testing in Combinatorial testing is a well-known black-box testing technique. It requires a set of test inputs so that it can apply the mechanism to produce new test inputs. Our main proposed approach involves the generation of test inputs for achieving branch coverage from Random testing values, Dictionary testing values, and a combination of both Random as well as Dictionary values with and without pairwise testing values. We have evaluated the effectiveness of our proposed approach using several experimental studies with baselines. The experimental results, on average, show that among all the approaches, the fusion of Random and Dictionary tests with Pairwise testing has superior results. Hence, this paper shows a new technique which is a healthy combination of two black-box and one white-box testing techniques which leads to Carbon-Box Testing.

关键词： Random Testing Dictionary Testing Combinatorial Testing Pairwise Testing branch coverage

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Traxtor: An Automatic Software Test Suit Generation Method Inspired by Imperialist Competitive Optimization Algorithms

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2022年第2期38卷 205-215页

作者： Arasteh, Bahman Hosseini, Seyed Mohamad Javad Istinye Univ Engn & Nat Sci Fac Dept Software Engn Istanbul Turkey Islamic Azad Univ Dept Comp Engn Sofian Branch Sofian Iran

Software testing refers to a process which improves the quality of software systems and also is one of time and cost consuming stages in software development. Hence, software test automation is regarded as a solution which can facilitate heavy and laborious tasks of testing. Automatic generation of test data with maximum coverage of program branches is regarded as an NP-complete optimization problem. Several heuristic and evolutionary algorithms have been proposed for generating test suits with maximum coverage. Failure to maximally branch coverage, poor success rate in test data generation with maximum coverage and lack of stable results are considered as the major drawbacks of previous methods. Enhancing the coverage rate of the generated test data, enhancing the success rate in generating the tests data with maximum coverage and enhancing the stability and speed criteria are the major purposes of the present study. In this study, an effective method (Traxtor) is proposed to automatically generate tests data by using imperialist competitive algorithms (ICA) optimization algorithms. The proposed method is aimed at generating test data with maximum branch coverage in a limited amount of time. The results obtained from executing a wide range of experiments indicated that the proposed algorithm, with 99.99% average coverage, 99.94% success rate, 2.77 average generation and 0.12 s average time outperformed the other algorithms.

关键词： Software testing Automatic test data generation Imperialist competitive algorithms branch coverage Success rate

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Shibboleth: Hybrid Patch Correctness Assessment in Automated Program Repair 7

Shibboleth: Hybrid Patch Correctness Assessment in Automated...

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37th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Ghanbari, Ali Marcus, Andrian Iowa State Univ Ames IA 50011 USA Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9781450394758

Test-based generate-and-validate automated program repair (APR) systems generate many patches that pass the test suite without fixing the bug. The generated patches must be manually inspected by the developers, a task that tends to be time-consuming, thereby diminishing the role of APR in reducing debugging costs. We present the design and implementation of a novel tool, named Shibboleth, for automatic assessment of the patches generated by test-based generate-and-validate APR systems. Shibboleth leverages lightweight static and dynamic heuristics from both test and production code to rank and classify the patches. Shibboleth is based on the idea that the buggy program is almost correct and the bugs are small mistakes that require small changes to fix and specifically the fix does not remove the code implementing correct functionality of the program. Thus, the tool measures the impact of patches on both production code (via syntactic and semantic similarity) and test code (via code coverage) to separate the patches that result in similar programs and that do not remove desired program elements. We have evaluated Shibboleth on 1,871 patches, generated by 29 Java-based APR systems for Defects4J programs. The technique outperforms state-of-the-art raking and classification techniques. Specifically, in our ranking data set, in 66% of the cases, Shibboleth ranks the correct patch in top-1 or top-2 positions and, in our classification data set, it achieves an accuracy and F1-score of 0.887 and 0.852, respectively, in classification mode. A demo video of the tool is available at https://***/3NvYJN8.

关键词： Automated Program Repair Patch Correctness Assessment Similarity branch coverage

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Patch Correctness Assessment in Automated Program Repair Based on the Impact of Patches on Production and Test Code 2022

Patch Correctness Assessment in Automated Program Repair Bas...

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31st ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)

作者： Ghanbari, Ali Marcus, Andrian Iowa State Univ Ames IA 50011 USA Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9781450393799

Test-based generate-and-validate automated program repair (APR) systems often generate many patches that pass the test suite without fixing the bug. The generated patches must be manually inspected by the developers, so previous research proposed various techniques for automatic correctness assessment of APR-generated patches. Among them, dynamic patch correctness assessment techniques rely on the assumption that, when running the originally passing test cases, the correct patches will not alter the program behavior in a significant way, e.g., removing the code implementing correct functionality of the program. In this paper, we propose and evaluate a novel technique, named Shibboleth, for automatic correctness assessment of the patches generated by test-based generate-and-validate APR systems. Unlike existing works, the impact of the patches is captured along three complementary facets, allowing more effective patch correctness assessment. Specifically, we measure the impact of patches on both production code (via syntactic and semantic similarity) and test code (via code coverage of passing tests) to separate the patches that result in similar programs and that do not delete desired program elements. Shibboleth assesses the correctness of patches via both ranking and classification. We evaluated Shibboleth on 1,871 patches, generated by 29 Java-based APR systems for Defects4J programs. The technique outperforms state-of-the-art ranking and classification techniques. Specifically, in our ranking data set, in 43% (66%) of the cases, Shibboleth ranks the correct patch in top-1 (top-2) positions, and in classification mode applied on our classification data set, it achieves an accuracy and F1-score of 0.887 and 0.852, respectively.

关键词： Automated Program Repair Patch Correctness Assessment Similarity branch coverage

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An HDL Simulator with Direct Register Access for Improving Code coverage 17

An HDL Simulator with Direct Register Access for Improving C...

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17th Annual Asia Joint Conference on Information Security (AsiaJCIS)

作者： Isawa, Ryoichi Kanaya, Nobuyuki Fujiwara, Yoshitada Takehisa, Tatsuya Ushimaru, Hayato Arisue, Dai Makita, Daisuke Mimura, Satoshi Inoue, Daisuke Natl Inst Informat & Commun Technol Koganei Tokyo Japan Nissin Inc Tokyo Japan Cyber Def Inst Inc Tokyo Japan

ISBN: (纸本)9781665473927

When debugging a DUT (Device Under Test) written in HDL (Hardware Description Language) code in simulation, code coverage is one of the most important evaluation metrics because it indicates how many unchecked statements remain where bugs could be hidden. A typical random test-pattern generator can be used very easily for debugging;however, it could fail to obtain enough code coverage of DUTs because it does not provide effective strategies for code coverage. In this paper, we propose an HDL simulator to improve branch coverage of DUTs up to 100%. A key idea behind our simulator is to directly write values to registers of DUTs for intentionally transfer a state to an unchecked state in the state machine of DUTs. This improves code coverage by executing statements corresponding to an unchecked state. Our simulator uses an SMT (Satisfiability Modulo Theories) solver to obtain the values written to registers from the condition (e.g., if and case) corresponding to an unchecked state. With the evaluation, we confirmed that our simulator successfully obtained a branch coverage of 100% for each of three open-sourced IP (Intellectual Property) core modules. As a bench mark, we also used a random test-pattern generator for those modules.

关键词： Verilog simulation branch coverage SMT solver debugging

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A language-parametric test coverage framework for executable domain-specific languages

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 211卷

作者： Khorram, Faezeh Bousse, Erwan Garmendia, Antonio Mottu, Jean-Marie Sunye, Gerson Wimmer, Manuel Univ Rennes CNRS Rennes France Nantes Univ Ecole Cent Nantes IMT Atlantique Nantes France Univ Autonoma Madrid Dept Ingn Informat Madrid Spain Johannes Kepler Univ Linz Inst Business Informat Software Engn CDL MINT Linz Austria

Test coverage is an effective technique to measure test case quality and to enable fault localization. However, for Executable Domain -Specific Languages (xDSLs), coverage metrics and associated tools are currently manually defined for each xDSL resulting in costly, error -prone, and non -reusable work. To address this problem, we propose a novel language -parametric test coverage framework for xDSLs. We define two coverage metrics adapted to xDSLs: model element and branch coverage. For performing coverage measurements, we propose a generic technique which can be used out -of -the -box by domain experts using any xDSL to define, execute, and test models. In addition, the coverage of model elements and branches can be parameterized for a given xDSL through the definition of coverage rules using a dedicated language. We showcase two test coverage applications for xDSLs: measuring the quality of test suites for executable models and localizing the models' faults using Spectrum -Based Fault Localization techniques. We evaluate our approach using four different xDSLs. Results show that (i) we can generate meaningful coverage measurements for all investigated models, (ii) the provided coverage rule language enables framework parameterization for all xDSLs, and (iii) the computed coverage measurements are useful in identifying defects of the models.

关键词： Model testing Model coverage branch coverage Fault localization Executable domain-specific languages Executable models

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Fuzuli: Automatic Test Data Generation for Software Structural Testing using Grey Wolf Optimization Algorithm and Genetic Algorithm 20

Fuzuli: Automatic Test Data Generation for Software Structur...

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IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)

作者： Arasteh, Bahman Sattari, Mohammad Reza Kalan, Reza Shokri Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkey Roshdiyeh Univ Dept Comp Tabriz Iran Digiturk beIN Media Grp Istanbul Turkey

ISBN: (数字)9781665462976

ISBN: (纸本)9781665462976

Software testing refers to a process that improves the quality of software systems through bug detection, but software testing is one of the time and cost-consuming stages in software development. Hence, software test automation is regarded as a solution, which can facilitate heavy and laborious tasks of testing. Problem: Automatic generation of data with maximum coverage of program branches is regarded as an NP-complete optimization problem. Several heuristic algorithms have been proposed for this problem. Failure to maximise branch coverage, the poor success rate in optimal test data generation, and low stable results are the major demerits of the previous methods. Goal: Enhancing the branch coverage rate of the generated test data, enhancing the success rate in generating the test data with maximum coverage, and enhancing the stability and speed criteria are the main goals of this study. Method: In this study, a combination of grey wolf optimization algorithm and genetic algorithm have been used to automatically generate optimal test data. The proposed hybrid method (Fuzuli(1)) tries to generate test data with maximum branch coverage at the software source code level. Results: The results obtained from the proposed algorithm were compared with those of the following algorithms: Shuffled Frog Leaping Algorithm (SFLA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).The results obtained from running a wide range of tests on standard benchmark programs showed that the proposed algorithm outperforms other algorithms with an average coverage of %99.98, a success rate of %99.97, and an average output of 2.86.

关键词： Test data generation branch coverage Stability GWO

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HOTCP: Hybrid Optimal Test Case Prioritisation with Multi-Objective Constraints

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2024年第3期23卷

作者： Wagh, Mukund Baburao Puri, Vishal V. Waykar, Sanjay B. Kadu, Rajesh Trinity Acad Engn Dept Comp Engn Pune 411048 Maharashtra India DY Patil Univ Sch Engn & Technol Dept Comp Engn Pune India Mahatma Gandhi Missions Coll Engn & Technol Dept Informat Technol Navi Mumbai Maharashtra India Mahatma Gandhi Missions Coll Engn & Technol Dept Comp Engn Navi Mumbai Maharashtra India

As a result of late detection and resource limitations during any software evaluation, there have been several software-related breakdowns or malfunctions. Many people have begun focussing on the test cases or alternatively the priority of validation suites after identifying the difficulties in the regression testing process of any product. The test case prioritisation technique is presented as a solution to this problem. It increases the fault detection rate. Earlier research studies have been implemented many techniques, but the rate of fault detection is not up to the mark. To overcome this drawback, we proposed HOTCP (Hybrid Optimal Test Case Prioritisation with Multi-Objective Constraints) model, which includes two steps: first is test case generation and the next one is test case prioritisation. The test case is generated from the released software. Consequently, test case prioritisation will be done by the optimisation strategy, in which the multi-objective function will be defined based on the constraints like statement coverage, branch coverage, contribution index and fault exposing potential. For this optimisation process, a new algorithm is proposed termed as CCCOA (Customised Coot and Chimp Optimisation Algorithm). The COOT optimisation and Chimp optimisation algorithms are combined in this algorithm. The system produces prioritised test cases, and the performance of the proposed method is validated in comparison to the traditional methods in terms of several metrics.

关键词： Statement coverage fault exposing potential branch coverage contribution index

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Shibboleth: Hybrid Patch Correctness Assessment in Automated Program Repair 22

Shibboleth: Hybrid Patch Correctness Assessment in Automated...

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

作者： Ali Ghanbari Andrian (Andi) Marcus Iowa State University United States of America The University of Texas at Dallas United States of America

ISBN: (纸本)9781450394758

关键词： branch coverage Similarity Automated Program Repair Patch Correctness Assessment

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