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Generating Test Data for Software Structural Testing Based on Particle Swarm Optimization

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014年第6期39卷 4593-4607页

作者： Mao, Chengying Jiangxi Univ Finance & Econ Sch Software & Commun Engn Nanchang 330013 Peoples R China

Testing is an important way to ensure and improve the quality of software system. However, it is a time-consuming and labor-intensive activity. In the paper, our main concern is software structural testing, and propose a search-based test data generation solution. In our framework, particle swarm optimization (PSO) technique is adopted due to its simplicity and fast convergence speed. For test data generation problem, the inputs of program under test are encoded into particles. Once a set of test inputs is produced, coverage information can be collected by test driver. Meanwhile, the fitness value of branch coverage can be calculated based on such information. Then, the fitness is used for PSO to adjust the search direction. Finally, the test data set with the highest coverage rate is yielded. In addition, eight well-known programs are used for experimental analysis. The results show that PSO-based approach has a distinct advantage compared to the traditional evolutionary algorithms such as generic algorithm and simulated annealing, and also outperforms comprehensive learning-PSO both in covering effect and in evolution speed.

关键词： Test data generation PSO branch coverage Fitness function Convergence speed

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Dual-Purpose Mixed-Level Test Generation Using Swarm Intelligence 23

Dual-Purpose Mixed-Level Test Generation Using Swarm Intelli...

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23rd IEEE Asian Test Symposium (ATS)

作者： Gent, Kelson Hsiao, Michael S. Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA

ISBN: (纸本)9781479960309

Automatic test pattern generation for non-scan sequential circuits is an extremely challenging task. If successful, it can offer many benefits to the EDA community, ranging from manufacturing and functional test to post-silicon validation. High-level test generators often miss the low-level details, thus missing the detection of some gate-level faults. On the other hand, gate-level test generators miss the high-level path traversal knowledge to more effectively traverse the state space. In this work, we present a fine-grain mixed-level test generator that utilizes co-simulation of register-transfer and gate levels to generate high quality vectors. The algorithm, based on an ant colony optimization, targets branch coverage at the RTL and simultaneously attempts to associate rare fault excitations with a sequence of branch activations. By weighting these sequences within the fitness function across the two levels, the algorithm is able to achieve high fault coverage in the presence of deep hard-to-reach states without scan. The result is that the test sequences obtained offer both high branch coverage as well high stuck-at coverage with low computational costs. In particular, for hard-to-test circuits such as the ITC'99 circuit b12, >98% branch coverage and >90% stuck-at coverage are achieved, vastly improving over other state of the art non-scan tools.

关键词： Automatic test pattern generation Circuit faults Generators Integrated circuit modeling Logic gates Mathematical model Vectors ATPG Ant Colony Optimization branch coverage ITC99 Mixed Level Test RTL Test Stuck-at Fault Swarm Intelligence co-simulation automatic test pattern generation Particle swarm optimization Circuit faults dynamos integrated circuit modelling Ant colony optimization Logic gates coverage Test generation Mathematical Model Stuck at faults fault coverage

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Method Sequence Generation for Multiple Object States using Dynamic Symbolic Execution

Method Sequence Generation for Multiple Object States using ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Takamatsu, Hiroki Sato, Haruhiko Oyama, Satoshi Kurihara, Masahito Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan

ISBN: (纸本)9781479938407

Software testing in object-oriented programming requires not only test input data, but also method sequences. Method sequences create new instances and change object states as desired. Method sequence generation to get desired object states is challenging. Seeker is one of the state-of-the-art implementation to generate test cases with method sequences. However, the technique generates method sequences to change a value of only a single variable. Therefore, it cannot cover the branches that need to modify values of multiple variables. In this paper, we extend the technique in order to cover branches which require multiple desired object states. The proposed approach detects all related variables in uncovered branches and uses a fitness function to give method sequences an evaluation value to reduce candidate methods. We applied Seeker and the proposed approach to four open source projects. The result shows a 1-5% improvememt of branch coverage over Seeker and also indicates that the effectiveness of the proposed approach varies depending on some specific features of projects.

关键词： object-oriented programming program testing Seeker branch coverage candidate methods dynamic symbolic execution fitness function method sequence generation multiple desired object states open source projects software testing Algorithm design and analysis Conferences Explosions Heuristic algorithms Libraries Software testing

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A Large-Scale Evaluation of Automated Unit Test Generation Using EvoSuite

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2014年第2期24卷 8-8页

作者： Fraser, Gordon Arcuri, Andrea Univ Sheffield Dept Comp Sci Regent Court Sheffield S1 4DP S Yorkshire England Certus Software V&V Ctr Simula Res Lab Lysaker Norway

Research on software testing produces many innovative automated techniques, but because software testing is by necessity incomplete and approximate, any new technique faces the challenge of an empirical assessment. In the past, we have demonstrated scientific advance in automated unit test generation with the EVOSUITE tool by evaluating it on manually selected open-source projects or examples that represent a particular problem addressed by the underlying technique. However, demonstrating scientific advance is not necessarily the same as demonstrating practical value;even if EVOSUITE worked well on the software projects we selected for evaluation, it might not scale up to the complexity of real systems. Ideally, one would use large "real-world" software systems to minimize the threats to external validity when evaluating research tools. However, neither choosing such software systems nor applying research prototypes to them are trivial tasks. In this article we present the results of a large experiment in unit test generation using the EVOSUITE tool on 100 randomly chosen open-source projects, the 10 most popular open-source projects according to the SourceForge Web site, seven industrial projects, and 11 automatically generated software projects. The study confirms that EVOSUITE can achieve good levels of branch coverage (on average, 71% per class) in practice. However, the study also exemplifies how the choice of software systems for an empirical study can influence the results of the experiments, which can serve to inform researchers to make more conscious choices in the selection of software system subjects. Furthermore, our experiments demonstrate how practical limitations interfere with scientific advances, branch coverage on an unbiased sample is affected by predominant environmental dependencies. The surprisingly large effect of such practical engineering problems in unit testing will hopefully lead to a larger appreciation of work in this area, thus support

关键词： Experimentation Reliability Unit testing automated test generation branch coverage empirical software engineering JUnit Java benchmark

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Towards Automatic Generation of Test Data using branch coverage

Towards Automatic Generation of Test Data using Branch Cover...

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4th International Conference on Computer Science and Education

作者： Chen, Jifeng Yang, Luming Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9781424435197

By analyzing various methods of automatic generation of test data using branch coverage, their characteristics and disadvantages are discussed, a new algorithm for automatic generation of test data is proposed. Through constructing the new procedure flow chart, the algorithm optimizes the selection paths using Fibonacci law, and generates test data for assigned branch. When the branch predicates of the chosen path are linear representation, solve the linear restraint set directly to generate test data otherwise determine that the path is inaccessible;When the branch predicate composing of nonlinear expression, linearize nonlinear function by using the divided difference approximate derivative to ensure the test data can easily generated through simple iteration, or conclude that path is inaccessible to a large extent. If the chosen path is to a large extent inaccessible or inaccessible, then a new path is selected, repent the above process until the desired data obtained, if no new path was chosen, then the specified branch was inaccessible. Experiments show that the algorithm is feasible and valid.

关键词： branch coverage Predicate function Linear arithmetic representation

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Is Depth of Inheritance Tree a Good Cost Prediction for branch coverage Testing?

Is Depth of Inheritance Tree a Good Cost Prediction for Bran...

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1st International Conference on Advances in System Testing and Validation Lifecycle

作者： Shaheen, Muhammad Rabee du Bousquet, Lydie Univ Grenoble LIG Grenoble France

ISBN: (纸本)9781424448623

Depth of Inheritance Tree (DIT) is supposed to be a factor influencing the cost of testing: test would be more expensive if DIT is large. A question is thus to know whether DIT can be used as a predictive metric to estimate the cost of testing. In this paper, we consider the cost of testing as the number of test cases required to achieve the branch coverage, which is a classical criterion for structural testing, and which is given by McCabe's Cyclomatic Complexity metric. We analyzed 25 applications to identify if the DIT is good test cost indicator. This paper shows that DITA is too abstract to be really relevant to predict the cost of testing.

关键词： testing cost testability branch coverage

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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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Estimating software testing complexity

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INFORMATION AND SOFTWARE TECHNOLOGY 2013年第12期55卷 2125-2139页

作者： Ferrer, Javier Chicano, Francisco Alba, Enrique Univ Malaga Dept Lenguajes & Ciencias Computac ETS Ingn Informat E-29071 Malaga Spain

Context: Complexity measures provide us some information about software artifacts. A measure of the difficulty of testing a piece of code could be very useful to take control about the test phase. Objective: The aim in this paper is the definition of a new measure of the difficulty for a computer to generate test cases, we call it branch coverage Expectation (BCE). We also analyze the most common complexity measures and the most important features of a program. With this analysis we are trying to discover whether there exists a relationship between them and the code coverage of an automatically generated test suite. Method: The definition of this measure is based on a Markov model of the program. This model is used not only to compute the BCE, but also to provide an estimation of the number of test cases needed to reach a given coverage level in the program. In order to check our proposal, we perform a theoretical validation and we carry out an empirical validation study using 2600 test programs. Results: The results show that the previously existing measures are not so useful to estimate the difficulty of testing a program, because they are not highly correlated with the code coverage. Our proposed measure is much more correlated with the code coverage than the existing complexity measures. Conclusion: The high correlation of our measure with the code coverage suggests that the BCE measure is a very promising way of measuring the difficulty to automatically test a program. Our proposed measure is useful for predicting the behavior of an automatic test case generator. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Evolutionary testing Complexity branch coverage Search based software engineering Evolutionary algorithms Testability

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Whole Test Suite Generation

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2013年第2期39卷 276-291页

作者： Fraser, Gordon Arcuri, Andrea Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England Certus Software V&V Ctr Simula Res Lab Lysaker Norway

Not all bugs lead to program crashes, and not always is there a formal specification to check the correctness of a software test's outcome. A common scenario in software testing is therefore that test data are generated, and a tester manually adds test oracles. As this is a difficult task, it is important to produce small yet representative test sets, and this representativeness is typically measured using code coverage. There is, however, a fundamental problem with the common approach of targeting one coverage goal at a time: coverage goals are not independent, not equally difficult, and sometimes infeasible-the result of test generation is therefore dependent on the order of coverage goals and how many of them are feasible. To overcome this problem, we propose a novel paradigm in which whole test suites are evolved with the aim of covering all coverage goals at the same time while keeping the total size as small as possible. This approach has several advantages, as for example, its effectiveness is not affected by the number of infeasible targets in the code. We have implemented this novel approach in the EVOSUITE tool, and compared it to the common approach of addressing one goal at a time. Evaluated on open source libraries and an industrial case study for a total of 1,741 classes, we show that EVOSUITE achieved up to 188 times the branch coverage of a traditional approach targeting single branches, with up to 62 percent smaller test suites.

关键词： Search-based software engineering length branch coverage genetic algorithm infeasible goal collateral coverage

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Towards Automatic Generation of Test Data using branch coverage

Towards Automatic Generation of Test Data using Branch Cover...

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第四届国际计算机新科技与教育学术会议(2009 4th International Conference on Computer Science & Education)

作者： Jifeng CHEN Luming YANG School of Information Science and Engineering Central South University Changsha 410083 China

By analyzing various methods of automatic generation of test data using branch coverage, their characteristics and disadvantages are discussed, a new algorithm for automatic generation of test data is proposed. Through constructing the new procedure flow chart, the algorithm optimizes the selection paths using Fibonacci law, and generates test data for assigned branch. When the branch predicates of the chosen path are linear representation, solve the linear restraint set directly to generate test data ,otherwise determine that the path is inaccessible;When the branch predicate composing of nonlinear expression, linearize nonlinear function by using the divided difference approximate derivative to ensure the test data can easily generated through simple iteration, or conclude that path is inaccessible to a large extent. If the chosen path is to a large extent inaccessible or inaccessible, then a new path is selected, repeat the above process until the desired data obtained, if no new path was chosen, then the specified branch was inaccessible. Experiments show that the algorithm is feasible and valid.

关键词： branch coverage Predicate function Linear arithmetic representation

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