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Test case generation using improved differential evolution algorithms with novel hypercube-based learning strategies

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期112卷

作者： Su, Qinghua Cai, Gaocheng Hu, Zhongbo Yang, Xianshan Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

Search-based algorithms are a recent research hotspot for solving path coverage (PC), which is the most critical and challenging problem in the field of automated test case generation (ATCG). There remains a large research space for achieving ATCG-PC's goal of finding a set of test cases covering all possible paths with as little computational overhead as possible. In contrast to two existing research approaches of testing different search-based algorithms and developing different fitness functions, this paper proposes two learning strategies based on a hypercube (termed HBL and THBL), which are inspired by a problem-specific knowledge expressed by the mathematical formulas of different fitness functions for ATCG-PC. The hypercubes of HBL and THBL are developed via an opposition-based learning strategy around the current best solution. The two learning strategies can guide search-based algorithms to search for test cases that cover uncovered paths. Two improved differential evolutionary algorithms based on HBL and THBL are then proposed to solve ATCG-PC. Experimental studies on thirty instances generated by eight classical benchmark programs and six fog computing benchmark programs show that the proposed algorithms achieve highest path coverage with fewer test cases and less running time than some compared state-of-the-art algorithms.

关键词： Automated test case generation Differential evolution Hypercube-based learning strategy path coverage

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Automated test case generation based on differential evolution with node branch archive

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 156卷 107290-107290页

作者： Dai, Xiaohu Gong, Wenyin Gu, Qiong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Hubei Univ Arts & Sci Sch Comp Engn Xiangyang 441053 Peoples R China

Automatic test case generation (ATCG) is the active research topic in software testing engineering, which can greatly reduce the cost of software testing. In automated test case generation for path coverage (ATCG-PC) problem, since the relationship between test cases and paths is unknown, there are many redundant test cases in the test case set to meet the path coverage criteria. In many previous studies on search-based algorithms for ATCG-PC, researchers have focused on improving the search-based algorithms itself or designing a more suitable fitness function according to the coverage criteria. However, the relationship between test cases and paths can help search-based algorithms cover more paths. The values of some specific test case dimensions change, and offspring individuals may cover different paths. Inspired by this, we proposed a node branch archive strategy, which can record the relationship between node branch direction and the value of test case variables, and cover more paths through this driven search-based algorithms. The experimental results show that compared with other state-of-the-art algorithms, the differential evolution with node branch archive (NBAr-DE) can significantly reduce the number of redundant test cases.

关键词： Automated test case generation Search-based algorithms Differential evolution path coverage Node branch archive

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Automated Test Case Generation Based on Competitive Swarm Optimizer with Schema and Node Branch Archive

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2022年第3期29卷 915-925页

作者： Dai, Xiaohu Ning, Bin Gu, Qiong Hu, Chunyang Li, Shuijia Hubei Univ Arts & Sci Sch Comp Engn 296 Longzhong Rd Xiangyang 441053 Hubei Peoples R China China Univ Geosci Sch Comp Sci 388 Lumo Rd Wuhan 430074 Hubei Peoples R China

Software testing plays an important role in the software development life cycle, among which automated test case generation (ATCG) technology is widely concerned because of its low cost and high degree of automation. In the process of using search-based algorithms to solve the automated test case generation for path coverage (ATCG-PC), how to minimize the generation of redundant test cases under the premise of 100% path coverage has always been a challenge. Inspired by improving the search ability of the search-based algorithm itself and the prior knowledge in the field of ATCG-PC, we propose a competitive swarm optimizer with schema and node branch archive (SNBAr-CSO) algorithm to solve the problem of complex test case generation with multiple variables in nodes. On the basis of competitive swarm optimizer, this algorithm uses the prior knowledge of schema to find all variables that affect the direction of a node branch quickly, and uses node branch archive to record the relationship between node branch direction and variable value. The experimental results of 12 practical programs on iFogSim and CoreNLP show that compared with other newly proposed algorithms, SNBAr-CSO can greatly reduce the number of redundant test cases under the premise of 100% path coverage.

关键词： automated test case generation competitive swarm optimizer node branch archive path coverage schema

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ESSENT: an arithmetic optimization algorithm with enhanced scatter search strategy for automated test case generation

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INFORMATION SCIENCES 2024年 656卷

作者： Li, Xiguang Feng, Baolu Sun, Yunhe Hawbani, Ammar Alsamhi, Saeed Hammod Zhao, Liang Shenyang Aerosp Univ Sch Comp Sci Shenyang Peoples R China Univ Galway Insight Ctr Data Analyt Galway Ireland

As one of the main research tasks in software testing, automated test case generation based on path coverage (ATCG-PC) aims to achieve maximum path coverage with a minimized set of test cases. In ATCG-PC, the correlation among the dimensions of test cases is widely utilized in academia to minimize the search efforts of the search-based algorithm. Nevertheless, the information related to target path selection is not utilized, which leads to blind decision-making by the search-based algorithm during the target path selection. Therefore, this paper proposes an enhanced scatter search strategy by using opposition-based learning. An arithmetic optimization algorithm is also proposed to solve ATCG-PC based on the enhanced scatter search strategy, namely, ESSENT. The ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path, and generates new test cases that cover the target path by modifying the dimensions of the existing test cases. The performance of the ESSENT algorithm is evaluated on six iFogSim subprograms and six Stanford coreNLP subprograms. Experiment results show that the ESSENT algorithm achieves a higher convergence rate than other state-of-the-art algorithms. Furthermore, it enables maximum path coverage with fewer test cases.

关键词： Automated test case generation path coverage Arithmetic optimization algorithm path entropy

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Backtracking search optimization algorithm with dual scatter search strategy for automated test case generation

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第7期35卷

作者： Ren, Jingru Zhu, Wenming Shenzhen Inst Informat Technol Dept Interdisciplinary Shenzhen Guangdong Peoples R China

It is a challenge to design an effective algorithm utilizing problem features in automated test case generation for path coverage (ATCG-PC). A feature of ATCG-PC "similar paths are usually executed by similar test cases" was touched by a few scholars and can be further exploited to design more effective algorithms. Inspired by this feature, this paper proposes a two-stage local search strategy, denoted dual scatter search (DS) strategy, which concatenates two improved scatter search strategies with different search behaviors. The first stage aims to fully exploit the discovered test cases to search for desired test cases, and the latter stage aims to mine the unexploited areas of the first stage via using less computational overhead. Then, a backtracking search optimization algorithm with dual scatter search strategy (BSADS) is proposed, which incorporates DS strategy into the backtracking search optimization algorithm (BSA) with strong exploration capability. BSA is first introduced into the field of ATCG-PC. The performance of BSA-DS and some state-of-the-art algorithms is tested on twelve popular benchmark programs. Experimental studies demonstrate that BSA-DS achieves the highest path coverage with the fewest test cases and running time on at least eight out of the twelve programs.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Automated test case generation Backtracking search optimization algorithm Dual scatter search strategy path coverage

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Test Case Generation for Vulnerability Detection Using Genetic Algorithm

Test Case Generation for Vulnerability Detection Using Genet...

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2015 4th National Conference on Electrical,Electronics and Computer Engineering(NCEECE 2015)

作者： Bo Shuai Haifeng Li Jian Wang Quan Zhang Chaojing Tang School of Electronic Science and Engineering National University of Defense Technology

ISBN: (纸本)9781510819085

In order to elevate efficiency of traditional Fuzzing technique, a novel method using genetic algorithm is proposed based on path coverage and test cost. There are evidences that GA has been already successful in generating test cases. Considering path coverage as the test adequacy criterion, we have designed a GA-based test data generator that is able to synthesize multiple test data to cover multiple target paths. Meanwhile, in order to reduce the test cost in Fuzzing process, test cost is analyzed respectively from running time and loop structure in the method. Experimental results show that proposed approach could obtain higher vulnerability detection accuracy and efficiency.

关键词： genetic algorithm fuzz path coverage test cost

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Data Flow Based Test Case Generation Algorithm for Object Oriented Integration Testing

Data Flow Based Test Case Generation Algorithm for Object Or...

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2015 6th IEEE International Conference on Software Engineering and Service Science(ICSESS 2015)

作者： Shahzada Zeeshan Waheed Usman Qamar Department of Computer Engineering National University of Science and Technology

Testing is way of insuring quality of the product. Object oriented testing is challenging task. It becomes more intense in integration level when different units interact for proper functioning of system. These Interacting Objects must be in desired state to achieve the required coverage. This can be achieved by generating method sequences. Manually test case generation is a tedious job and in object oriented testing method explosion make it worst. In this paper we propose a novel approach for test case generation of object oriented integration testing. Search space is reduced by selecting the coupling methods that are directly involved in integration. Further def-use analysis helps in achieving the desired object states for proper interfaces testing, representing methods in testing tree. This paper also identifies problematic cases and their solutions in method sequence generation. Results shown significant improvements in object oriented integration testing.

关键词： path coverage Test criteria Test cases DU-analysis SUT Object Oriented Testing

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ET-based Test Data Generation for Multiple-path Testing

ET-based Test Data Generation for Multiple-path Testing

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2016 3rd International Conference on Engineering Technology and Application（ICETA 2016）

作者： Qingjie Wei Yijin Li Ying Qian College of Computer Science and Technology Chongqing University of Posts and Telecommunications College of Software Engineering Chongqing University of Posts and Telecommunications

Automatic test data generation for path testing is one of the difficult problems of software testing And the existing methods have low efficiency and *** have conducted studies on the research field about ET-based test data generation for multiple-path testing,and we have improved the efficiency and the stability of the method by improved the fitness function of currently existing *** the comparison experiments,it showed that the method in this paper have high efficiency and ***,we gave the conclusion how the basic parameters impacted on the efficiency,and suggested the setting range of parameters.

关键词： software testing evolutionary testing path coverage test data genetic algorithms

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On automatic generation of RTL validation test benches using circuit testing techniques 03

On automatic generation of RTL validation test benches using...

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Proceedings of the 13th ACM Great Lakes symposium on VLSI

作者： Indradeep Ghosh Srivaths Ravi Fujitsu Laboratories of America Sunnyvale CA NEC Laboratories America Princeton NJ

In this paper, we examine how good validation test benches can be automatically generated starting from the RTL description of a circuit. We develop our methodology based on extensive experiments performed with severa... 详细信息

ISBN: (纸本)9781581136777

关键词： coverage metrics RTL testing test test sets branch coverage universal test sets fault coverage path coverage OCCOM toggle coverage RTL ATPG design validation small validation testing generation ATPG testbench code coverage

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Efficient approach to simulation vector generation using Bayesian network

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics 2007年第5期19卷 616-621页

作者： Yi, Jiangfang Tong, Dong Cheng, Xu Micro-Processor Research and Development Center Peking University Beijing 100871 China

Improving the efficiency of simulation-based validation is important. Most of simulation vectors for regression test are huge and unnecessary. They made the covering process inefficient. In this paper, we used Bayesian network to describe the relation between the inputs and the branch statements. The new simulation vectors were generated by reasoning on the network. We performed experiments on some functional modules. The results indicate that the average vector length generated by the Bayesian network using different reference algorithms is about 10% of the original one, but the best path coverage even exceeds the original one.

关键词： Bayesian network Generation of simulation vectors Machine learning path coverage Validation

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