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8th International Conference on Software Security and Reliability (SERE)

作者： Wu, Tianyong Yan, Jun Zhang, Jian Chinese Acad Sci Inst Software Technol Ctr Software Engn Beijing 100864 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100864 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781479942961

Modified Condition/Decision Coverage (MC/DC) became widely used in software testing, especially in safety-critical domain. However, existing testing tools often aim at achieving statement or branch coverage and do not support test generation for MC/DC. In this paper, we propose a novel test generation method to find appropriate test data for MC/DC. Specifically, we first extract paths from the target program and then find appropriate test data to trigger these paths. In the path extraction process, we propose a greedy strategy to determine the next selected branch. The evaluation results show that our method can actually generate test data quickly and the coverage increases a lot (up to 37.5%) compared with existing approaches.

关键词： unit testing test data generation MC/DC

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A New Method of test data generation for Branch Coverage in Software testing Based on EPDG and Genetic Algorithm

A New Method of Test Data Generation for Branch Coverage in ...

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3rd International Conference on Anti-Counterfeiting, Security, and Identification in Communication

作者： Chen, Ciyong Xu, Xiaofeng Chen, Yan Li, Xiaochao Guo, Donghui Xiamen Univ Dept Phys Xiamen 361005 Peoples R China Xiamen Univ Sch Informat Sci & Technol Xiamen 361005 Peoples R China

ISBN: (纸本)9781424438822

A new method called EPDG-GA which utilizes the Edge Partitions Dominator Graph (EPDG) and Genetic Algorithm (GA) for branch coverage testing is presented in this paper. First, a set of Critical Branches (CBs) are obtained by analyzing the EPDG of the tested program, while covering all the CBs implies covering all the branches of the Control Flow Graph (CFG). Then, the fitness functions are instrumented in the right position by analyzing the Pre-Dominator Tree (PreDT), and two metrics are developed to prioritize the CBs. Coverage-Table is established to record the CBs information and keeps track of whether a branch is executed or not. GA is used to generate test data to cover CBs so as to cover all the branches. The comparison results show that this approach is more efficient than random testing approach.

关键词： test data generation Edge Partitions Genetic Algorithm Branch Coverage

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Multimodal Multi-Objective test data generation Method based on Particle Swarm Optimization 24

Multimodal Multi-Objective Test Data Generation Method based...

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24th IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Yao, Qi Zhang, Yizhuo Li, Yujia Liu, Fang Yang, Shunkun Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China China Elect Power Res Inst Beijing Peoples R China

ISBN: (纸本)9798350365634

Cyber-Physical Systems (CPS) confront significant challenges in the assessment of state after experiencing disturbances or attacks, attributed to their inherent complexity. This situation demands comprehensive and expensive experiments for evaluation. Employing black-box optimization methods to optimize test data generation proves efficacious. Nevertheless, prevailing black-box optimization techniques often prioritize trade-offs among objectives, neglecting the search space's multimodality. To bridge this divide, we draw inspiration from multi-objective multimodal optimization problems (MMOPs) to address black-box optimization problems, proposing a multimodal multi-objective test data generation method (MMO-TDG) for testing the state of CPS under disturbances and attacks. The clustering-based particle swarm optimization leveraging adaptive resonance theory, termed CARTPSO, is employed to solve MMOPs in the test data generation process. Experiment results demonstrate that CARTPSO shows significantly superior performance to five leading multimodal multi-objective algorithms across 11 benchmark functions. A novelty co-simulation testing environment is built for testing the state of aircraft encountering wind disturbance in a blackbox manner. The proposed MMO-TDG is applied in this environment to generate test data against random search and NSGAII-based test data generation method. Results show that test data generated by MMO-TDG not only exhibit diversity but also effectively fulfill the testing objectives.

关键词： Cyber-Physical Systems test data generation black-box optimization multimodal multi-objective optimization wind disturbance

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Hybrid test data generation 2014

Hybrid Test Data Generation

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36th International Conference on Software Engineering (ICSE)

作者： Liu, Zicong Chen, Zhenyu Fang, Chunrong Shi, Qingkai Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781450327688

Many automatic test data generation techniques have been proposed in the past decades. Each technique can only deal with very restrictive data types so far. This limits the usefulness of test data generation in practice. We present a preliminary approach on hybrid test data generation, by combining Random Strategy (RS), Dynamic Symbolic Execution (DSE), and Search-based Strategy (SBS). It is expected to take advantage of the state-of-the-arts to enhance the robustness and scalability, in terms of different types of test data.

关键词： Verification Reliability Hybrid strategy test data generation random strategy dynamic symbolic execution search-based strategy

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Evolutionary Algorithm with Convergence Speed Controller for Automated Software test data generation Problem

Evolutionary Algorithm with Convergence Speed Controller for...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Liu, Fangqing Huang, Han Hao, Zhifeng Univ Techol Software Engn Sch South China Guangzhou 510006 Guangdong Peoples R China Foshan Univ Sch Math & Big Data POB Foshan Peoples R China

ISBN: (纸本)9781509046010

Software testing is an important process of software development. One of the challenges in testing software is to generate test cases which help to reveal errors. Automated software test data generation problem is hard because it needs to search the whole feasible area to find test cases covering all possible paths under acceptable time consumption. In this paper, evolutionary algorithm with convergence speed controller (EA-CSC) is presented for using the least test case overhead in solving automated test case generation problem. EA-CSC is designed as a framework which have fast convergence speed and capability to jump out of the local optimal solution over a range of problems. There are two critical steps in EA-CSC. The adaptive step size searching method accelerates the convergence speed of EA. The mutation operator can disrupt the population distribution and slows down the convergence process of EA. Moreover, the EA-CSC results are compared to the algorithms tested on the same benchmark problems, showing strong competitive.

关键词： Evolutionary Algorithm(EA) Convergence Speed Controller(CSC) test data generation

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Automated test data generation for Aspect-Oriented Programs 09

Automated Test Data Generation for Aspect-Oriented Programs

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8th International Conference on Aspect-Oriented Software Development

作者： Harman, Mark Islam, Fayezin Xie, Tao Wappler, Stefan Kings Coll London Dept Comp Sci London WC2R 2LS England

ISBN: (纸本)9781605584423

Despite the upsurge of interest in the Aspect-Oriented Programming (AOP) paradigm, there remain few results on test data generation techniques for AOP Furthermore, there is no work on search-based optimization for test data generation, an approach that has been shown to be successful in other programming paradigms. In this paper, we introduce a search-based optimization approach to automated test data generation for structural coverage of AOP systems. We present the results of an empirical study that demonstrates the effectiveness of the approach. We also introduce a domain reduction approach for AOP testing and show that this approach not only reduces test effort, but also increases test effectiveness. This finding is significant, because similar studies for non-AOP programming paradigms show no such improvement in effectiveness, merely a reduction in effort. We also present the results of an empirical study of the reduction in test effort achieved by focusing specifically on branches inside aspects.

关键词： test data generation aspect-oriented software development evolutionary testing search-based software engineering

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test data generation for covering mutation-based path using MGA for MPI program

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 210卷

作者： Dang, Xiangying Wang, Jinyong Gong, Dunwei Yao, Xiangjuan Wei, Changqing Xu, Biao Xuzhou Univ Technol Sch Informat Engn Sch Big Data Xuzhou 221018 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China China Univ Min & Technol Sch Math Xuzhou 221116 Peoples R China Shantou Univ Dept Elect Engn Shantou 515063 Peoples R China

Message Passing Interface (MPI) is a communication protocol used for parallel programming in various languages, valued for its reliability and broad applicability. Mutation testing is a software testing method for systematically simulating software faults. However, the significant number of inserted mutation branches in the program escalates the testing cost. To address this issue, we propose the comprehensive PMTDGM framework. The framework first generates mutation -based paths based on the relevancy of mutant branches and the difficulty of mutant branch coverage, followed by the establishment of a multitask model of path coverage. Finally, we employ a multi -population genetic algorithm (MGA) to generate test data. Our experiments, performed on six MPI programs of varying sizes and structures, demonstrate that the mutation -based paths have the small test sets and are easy to cover. Additionally, the multitask model and MGA can significantly improve the efficiency of generating test data and reduce the cost of mutation testing for MPI programs compared to traditional methods.

关键词： Message passing interface (MPI) Software testing Mutation testing Multi-population genetic algorithm (MGA) test data generation

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Bio-Inspired Optimization of test data generation for Concurrent Software 11th

Bio-Inspired Optimization of Test Data Generation for Concur...

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11th International Symposium on Search-Based Software Engineering (SSBSE)

作者： Vilela, Ricardo F. Pinto, Victor H. S. C. Colanzi, Thelma E. Souza, Simone R. S. Univ Sao Paulo ICMC USP Inst Math & Comp Sci Trabalhador Sao Carlense Ave 400 BR-13566590 Sao Carlos SP Brazil Univ Estadual Maringa UEM Informat Dept Av Colombo 5790Zona 7 BR-87020900 Maringa Parana Brazil

ISBN: (纸本)9783030274559;9783030274542

Concurrent software includes a number of key features such as communication, concurrency, and non-determinism, which increase the complexity of software testing. One of the main challenges is the test data generation. Techniques of search-based software can also benefit concurrent software testing. To do so, this paper adopts a bio-inspired approach, called BioConcST, to support the automatic test data generation for concurrent programs. BioConcST uses a Genetic Algorithm (GA) and an evolutionary strategy adapted to accept genetic information from some bad individuals (test data) in order to generate better individuals. Structural testing criteria for concurrent programs are used to guide the evolution of test data generation. An experimental study was carried out to compare BioConcST with an elitist GA strategy (EGA) in terms of adequacy of testing criteria for message-passing and share-dmemory programs. Twelve concurrent Java programs were included and the results suggest BioConcST is a promising approach, since in all the testing criteria evaluated, it achieved a better coverage and the effect-size measure was large in most cases.

关键词： Concurrent software testing Structural testing Search-based software testing Genetic algorithm test data generation

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The Use of Automatic test data generation for Genetic Improvement in a Live System 10

The Use of Automatic Test Data Generation for Genetic Improv...

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IEEE/ACM 10th International Workshop on Search-Based Software testing (SBST)

作者： Haraldsson, Saemundur O. Woodward, John R. Brownlee, Alexander I. E. Univ Stirling Dept Math & Comp Sci Stirling Scotland

ISBN: (纸本)9781538627891

In this paper we present a bespoke live system in commercial use that has been implemented with self-improving properties. During business hours it provides overview and control for many specialists to simultaneously schedule and observe the rehabilitation process for multiple clients. However in the evening, after the last user logs out, it starts a self-analysis based on the day's recorded interactions and the self-improving process. It uses Search Based Software testing (SBST) techniques to generate test data for Genetic Improvement (GI) to fix any bugs if exceptions have been recorded. The system has already been under testing for 4 months and demonstrates the effectiveness of simple test data generation and the power of GI for improving live code.

关键词： Search Based Software Engineering test data generation Bug fixing Real world application

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MC/DC test data generation Algorithm Based on Whale Genetic Algorithm

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Instrumentation 2022年第2期9卷 1-12页

作者： LIU Huiying LIU Ziyang YAN Minghui North China Institute of Aerospace Engineering Langfang 065000

The automatic generation of test data is a key step in realizing automated *** automated testing tools for unit testing only provide test case execution drivers and cannot generate test data that meets coverage *** paper presents an improved Whale Genetic Algorithm for generating test data re-quired for unit testing MC/DC *** proposed algorithm introduces an elite retention strategy to avoid the genetic algorithm from falling into iterative *** the same time,the mutation threshold of the whale algorithm is introduced to balance the global exploration and local search capabilities of the genetic *** threshold is dynamically adjusted according to the diversity and evolution stage of current popu-lation,which positively guides the evolution of the ***,an improved crossover strategy is pro-posed to accelerate the convergence of the *** improved whale genetic algorithm is compared with genetic algorithm,whale algorithm and particle swarm algorithm on two benchmark *** results show that the proposed algorithm is faster for test data generation than comparison methods and can provide better coverage with fewer evaluations,and has great advantages in generating test data.

关键词： test data generation MC/DC Whale Genetic Algorithm Mutation Threshold

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