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Generating test data for both path coverage and fault detection using genetic algorithms

Frontiers of Computer Science 2013年第6期7卷 822-837页

作者： Dunwei GONG Yan ZHANG School of Information and Electrical Engineering China University of Mining and Technology Xuzhou 221116 China School of Technology Mudanjiang Normal University Mudanjiang 157012 China

The aim of software testing is to find faults in a program under test, so generating test data that can expose the faults of a program is very important. To date, current stud- ies on generating test data for path coverage do not perform well in detecting low probability faults on the covered path. The automatic generation of test data for both path coverage and fault detection using genetic algorithms is the focus of this study. To this end, the problem is first formulated as a bi-objective optimization problem with one constraint whose objectives are the number of faults detected in the traversed path and the risk level of these faults, and whose constraint is that the traversed path must be the target path. An evolution- ary algorithm is employed to solve the formulated model, and several types of fault detection methods are given. Finally, the proposed method is applied to several real-world programs, and compared with a random method and evolutionary opti- mization method in the following three aspects： the number of generations and the time consumption needed to generate desired test data, and the success rate of detecting faults. The experimental results confirm that the proposed method can effectively generate test data that not only traverse the target path but also detect faults lying in it.

关键词： software testing path coverage fault detection test data multi-objective optimization genetic algorithms

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Automated Test Case Generation for path coverage by Using Multi-Objective Particle Swarm Optimization Algorithm with Reinforcement Learning and Relationship Matrix Strategies

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2024年第8期34卷 1221-1249页

作者： Liang, Shuwen He, Zhitao Beihang Univ Sch Comp Sci & Engn 37 Xueyuan Rd Beijing 100191 Peoples R China

Software testing is an important phase in software life cycle, and test case generation occupies the main part of the testing workload. path coverage is the most stringent and difficult coverage criteria, so automatic test case generation for path coverage (ATCG-PC) is an important task in software testing. ATCG-PC is usually abstracted as an optimization search problem, and the commonly used solution algorithm is swarm intelligence algorithm. This paper proposes a path coverage test case generation algorithm based on improved multi-objective particle swarm optimization (IMOPSO) algorithm. The algorithm uses the multi-objective strategy to generate test cases for a group of similar paths at a time, and is supplemented by the particle action selection strategy based on reinforcement learning and the secondary search algorithm based on relationship matrix, which makes use of the similarity information between paths to speed up the early convergence and help to quickly reach the optimal solution around the local optimal position. By grouping the paths according to the similarity, the algorithm reduces the test case consumption, and improves the efficiency of test case generation. Experimental results show that, compared with the existing methods, the proposed algorithm can achieve higher path coverage with less test case consumption and is also suitable for large-scale programs.

关键词： Automated test case generation path coverage multi-objective particle swarm optimization reinforcement learning

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Test Data Generation for path coverage of MPI Programs Using SAEO

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2021年第2期30卷 p1-37页

作者： Gong, Dunwei Sun, Baicai Yao, Xiangjuan Tian, Tian China Univ Min & Technol Sch Informat & Control Engn 1 Univ Rd Xuzhou 221116 Jiangsu Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol 99 Songling Rd Qingdao 266061 Shandong Peoples R China China Univ Min & Technol Sch Math 1 Univ Rd Xuzhou 221116 Jiangsu Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Fengming Rd Jinan 250101 Shandong Peoples R China

Message-passing interface (MPI) programs, a typical kind of parallel programs, have been commonly used in various applications. However, it generally takes exhaustive computation to run these programs when generating test data to test them. In this article, we propose a method of test data generation for path coverage of MPI programs using surrogate-assisted evolutionary optimization, which can efficiently generate test data with high quality. We first divide a sample set of a program into a number of clusters according to the multi-mode characteristic of the coverage problem, with each cluster training a surrogate model. Then, we estimate the fitness of each individual using one or more surrogate models when generating test data through evolving a population. Finally, a small number of representative individuals are selected to execute the program, with the purpose of obtaining their real fitness, to guide the subsequent evolution of the population. We apply the proposed method to seven benchmark MPI programs and compare it with several state-of-the-art approaches. The experimental results show that the proposed method can generate test data with reduced computation, thus improving the testing efficiency.

关键词： MPI program path coverage test data generation evolutionary optimization surrogate model

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Test data generation for path coverage of message-passing parallel programs based on co-evolutionary genetic algorithms

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AUTOMATED SOFTWARE ENGINEERING 2016年第3期23卷 469-500页

作者： Tian, Tian Gong, Dunwei China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China Lanzhou Univ Technol Sch Elect Engn & Informat Engn Lanzhou 730050 Gansu Peoples R China

Employing genetic algorithms to generate test data for path coverage has been an important method in software testing. Previous work, however, is suitable mainly for serial programs. Automatic test data generation for path coverage of message-passing parallel programs without non-determinacy is investigated in this study by using co-evolutionary genetic algorithms. This problem is first formulated as a single-objective optimization problem, and then a novel co-evolutionary genetic algorithm is proposed to tackle the formulated optimization problem. This method employs the alternate co-evolution of two kinds of populations to generate test data that meet path coverage. The proposed method is applied to seven parallel programs, and compared with the other three methods. The experimental results show that the proposed method has the best success rate and the least number of evaluated individuals and time consumption.

关键词： Software testing Test data path coverage Parallel program Co-evolutionary genetic algorithm

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Improving Test Data Generation for MPI Program path coverage With FERPSO-IMPR and Surrogate-Assisted Models

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2024年第3期50卷 495-511页

作者： Wang, Yong Cui, Wenzhong Wang, Gai-Ge Wang, Jian Gong, Dunwei Ocean Univ China Sch Comp Sci & Technol Qingdao 266100 Peoples R China China Univ Petr East China Coll Sci Qingdao 266580 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China

Message passing interface (MPI) is a powerful tool for parallel computing, originally designed for high-performance computing on massively parallel computers. In this paper, we combine FERPSO-IMPR (fitness Euclidean distance ratio particle swarm optimizer with information migration-based penalty and population reshaping) and surrogate-assisted models to generate test cases for MPI program path coverage testing. In our proposed method, FERPSO-IMPR employs a dual population strategy to initialize data and calculate fitness. Then, we create a sample set based on the initial data and its fitness. Subsequently, we train the master-slave surrogate models to predict individual fitness. Finally, a small number of elite individuals are selected to execute the program to decide whether to generate the required test data and guide the subsequent evolution process. We apply the proposed method to seven MPI programs and perform experimental comparisons from five directions. Experimental results show that compared with the comparative method, the time consumption of the proposed method is reduced by 33.2%, the number of evaluations is reduced by 38.8%, and the success rate is increased by 7.6%. These results prove that our method can effectively reduce the test data generation cost of MPI programs.

关键词： Test data generation FERPSO-IMPR surrogate-assisted models path coverage MPI program

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path coverage Information for Adaptive Random Testing 2017

Path Coverage Information for Adaptive Random Testing

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International Conference on Information Technology (ICIT)

作者： Sinaga, Arnaldo Marulitua Hutajulu, Oscar Daniel Hutahaean, Ruth Tabita Hutagaol, Ida Christy Inst Teknol Del Toba Samosir Sumatera Utara Indonesia

ISBN: (纸本)9781450363518

This paper proposes the application of path coverage information into Adaptive Random Testing (ART). The ART is a distance-based technique. It selects the furthest test cases from the previous executed test cases. Applying path coverage is intended to provide good distance calculation between test cases. This is based on the intuition that the path coverage information ensure that all code's nodes have been covered, hence if there is a fault in a node it will be detected. A series of experiments have been conducted to investigate the effectiveness of this proposed method. As the benchmark, the Random Testing and Branch coverage method are implemented. Previously, Branch coverage has been found as one of the best coverage method for software testing. The studied methods were applied into a C program named Space. The F-measure is used to measure the effectiveness. The results of the experiments indicate that the path coverage with Adaptive Random Testing outperformed Random Testing and Branch coverage Method significantly. It is proved that the path coverage is a good information for Adaptive Random Testing.

关键词： Software Testing path coverage Adaptive Random Testing Test Case Selection

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path coverage based functional test generation for processor marginality validation 41

Path coverage based functional test generation for processor...

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International Test Conference 2010

作者： Natarajan, Suriyaprakash Krishnamachary, Arun Chiprout, Eli Galivanche, Rajesh Intel Corp Santa Clara CA 95052 USA Intel Corp Chandler AZ 85226 USA Intel Corp Hillsboro 80501 OR USA

ISBN: (纸本)9781424472055

Functional test content to screen for electrical marginalities during silicon validation are not generated with the goal of identifying speed-limiting paths, adversely affecting the quality and efficiency of validation We propose a methodology to generate functional tests to excite pre-silicon timing-critical paths along with environmental effects such as voltage droop. These tests are to replace random/function-targeted content as the source for identifying speed failures during silicon validation The effectiveness of this methodology

关键词： electrical marginality environmental effects functional test generation integrated circuit testing microprocessor chips path coverage pre-silicon timing-critical path processor marginality validation silicon validation speed failure identification voltage

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Search-Based Software Test Data Generation for path coverage Based on a Feedback-Directed Mechanism

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Complex System Modeling and Simulation 2023年第1期3卷 12-31页

作者： Stuart Dereck Semujju Han Huang Fangqing Liu Yi Xiang Zhifeng Hao School of Software Engineering South China University of TechnologyGuangzhou 510006China College of Science Shantou UniversityShantou 515063China

Automatically generating test cases by evolutionary algorithms to satisfy the path coverage criterion has attracted much research attention in software *** the context of generating test cases to cover many target paths,the efficiency of existing methods needs to be further improved when infeasible or difficult paths exist in the program under *** is because a significant amount of the search budget(i.e.,time allocated for the search to run)is consumed when computing fitness evaluations of individuals on infeasible or difficult *** this work,we present a feedback-directed mechanism that temporarily removes groups of paths from the target paths when no improvement is observed for these paths in subsequent *** fulfill this task,our strategy first organizes paths into ***,in each generation,the objective scores of each individual for all paths in each group are summed *** each group,the lowest value of the summed up objective scores among all individuals is assigned as the best aggregated score for a group.A group is removed when no improvement is observed in its best aggregated score over the last two *** experimental results show that the proposed approach can significantly improve path coverage rates for programs under test with infeasible or difficult paths in case of a limited search *** particular,the feedback-directed mechanism reduces wasting the search budget on infeasible paths or on difficult target paths that require many fitness evaluations before getting an improvement.

关键词： automated test case generation software testing path coverage many-objective optimization

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EPC: Extended path coverage for Measurement-based Probabilistic Timing Analysis 36

EPC: Extended Path Coverage for Measurement-based Probabilis...

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IEEE 36th Real-Time Systems Symposium (RTSS)

作者： Ziccardi, Marco Mezzetti, Enrico Vardanega, Tullio Abella, Jaume Cazorla, Francisco J. Univ Padua Dept Math I-35100 Padua Italy Barcelona Supercomp Ctr Madrid Spain Spanish Natl Res Council IIIA CSIC Madrid Spain

ISBN: (纸本)9781467395076

Measurement-based probabilistic timing analysis (MBPTA) computes trustworthy upper bounds to the execution time of software programs. MBPTA has the connotation, typical of measurement-based techniques, that the bounds computed with it only relate to what is observed in actual program traversals, which may not include the effective worst-case phenomena. To overcome this limitation, we propose Extended path coverage (EPC), a novel technique that allows extending the representativeness of the bounds computed by MBPTA. We make the observation data probabilistically path-independent by modifying the probability distribution of the observed timing behaviour so as to negatively compensate for any benefits that a basic block may draw from a path leading to it. This enables the derivation of trustworthy upper bounds to the probabilistic execution time of all paths in the program, even when the user-provided input vectors do not exercise the worst-case path. Our results confirm that using MBPTA with EPC produces fully trustworthy upper bounds with competitively small overestimation in comparison to state-of-the-art MBPTA techniques.

关键词： Measurement-Based Probabilistic Timing Analysis path coverage Timing Analysis

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Maximum Gaps in path coverage 19

Maximum Gaps in Path Coverage

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22nd ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (ACM MSWiM)

作者： Shamoun, Simon Abdelzaher, Tarek Bar-Noy, Amotz CUNY New York NY 10021 USA Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781450369046

We study the maximum size of coverage gaps by sensors selected to cover a path. Gap sizes, and not just total coverage, are important because significant events can be missed during uncovered periods. The amount of knowledge about a path affects the ability to select sensors to cover it. We first study how coverage gaps are affected by increases in knowledge and improvements in selection strategies when sensors are selected to maximize path coverage. The gap size does not necessarily decrease in the same way that coverage increases with a better selection. We then show that even simple modifications to the algorithm can reduce the coverage gap, and show how this is affected about the level of knowledge.

关键词： sensor selection path coverage coverage gaps

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