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2nd International Conference on software testing, Verification and Validation Workshops

作者： Antoniol, Giuliano Ecole Polytech Montreal SOCCER Lab DGIGL Quebec City PQ Canada

ISBN: (纸本)9780769536712

Ensuring security of software and computerized systems is a pervasive problem plaguing companies and institutions and affecting many areas of modern life. software vulnerability may jeopardize information confidentiality and cause software failure leading to catastrophic threats to humans or severe economic losses. Size, complexity, extensibility, connectivity and the search for cheap systems make it very hard or even impossible to manually tackle vulnerability detection. search based software testing attempts to solve two aspects of the cost - vulnerability problem. First, it's cheaper because it is far less labor intensive when compared to traditional testing techniques. As a result, it can be used to more thoroughly test software and reduce the risk that a vulnerability slips into production code. Also, search based software testing can be specifically tailored to tackle the subset of well known security vulnerabilities responsible for most security threats. This paper is divided into two parts. It examines promising search based testing approaches to detecting software vulnerabilities, and then presents some of the most interesting open research problems.

关键词： search based software testing vulnerability exposure high dependability software

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search based software testing for the generation of synchronization sequences for mutation testing of concurrent programs

Search based software testing for the generation of synchron...

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作者： Rodolfo Adamshuk Silva UNIVERSIDADE DE SAO PAULO

学位级别：博士

Concurrent programming has become an essential paradigm for reductions in the computational time in many application domains. However, the validation and testing activity is more complex than the testing for sequential programs due to the non-determinism, synchronization and inter-process communication. Mutation testing is based on mistakes produced by software developers and presents a high effectiveness to reveal faults. However, high computational costs limit its applicability even for sequential code, becoming higher for concurrent programs in which each test has to be executed with different (ideally all) thread schedules. To date, only selective mutation have been applied to reduce the number of mutants in concurrent programs, however, the problem of state explosion of thread schedules still remains. This Ph. D. thesis presents the SBBMuT approach that applies deterministic execution and genetic algorithm for the generation and execution of a set of synchronization sequences during the mutation testing of Java multithreaded programs. An experimental study was conducted, and the results showed that the set of synchronization sequences generated by SBBMuT achieved a higher mutation score in comparison with the use of the Java PathFinder model checking tool.

关键词： Concurrent programming Mutation testing search based software testing

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A Dynamic Fitness Function based on Branch Hardness for search based software testing 25

A Dynamic Fitness Function Based on Branch Hardness for Sear...

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25th Asia-Pacific software Engineering Conference (APSEC)

作者： Zhu, Ziming Jiao, Li Xu, Xiong Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China

ISBN: (纸本)9781728119700

search based software testing (SBST) uses meta-heuristic optimizing algorithms to automate a testing task, for example the automatic generation of test data. The goal of SBST is to satisfy various test adequacy criteria. Among the different types of coverage criteria, branch coverage is the most used criterion in software testing. The fitness function is one of the most important parts in SBST. However, most of the researches for SBST focus on changing the search algorithms instead of improving fitness functions on which all heuristic search relies. This paper proposes a dynamic fitness function for branch coverage. Concretely, we evaluate the hardness of covering each essential branch, based on which an approximation algorithm is designed to evaluate the distance of a test candidate to the target branch. As the branch hardness varies with the search iteration, the heuristic information is obtained dynamically. Thus, this fitness function is richer and more informed than the traditional counterparts. Finally, the empirical study reveals the promising results of our proposal. Especially for the branches that are difficult to be covered, the fitness function proposed in this paper outperforms its counterparts significantly.

关键词： branch hardness dynamic fitness function search based software testing

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Objective Re-Weighting to Guide an Interactive search based software testing System

Objective Re-Weighting to Guide an Interactive Search Based ...

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12th International Conference on Machine Learning and Applications (ICMLA)

作者： Marculescu, Bogdan Feldt, Robert Torkar, Richard Blekinge Inst Technol Sch Comp Karlskrona Sweden Chalmers Gothenburg Sweden Univ Gothenburg Dept Comp Sci & Engn Gothenburg Sweden

ISBN: (纸本)9780769551449

Even hardware-focused industries today develop products where software is both a large and important component. Engineers tasked with developing and integrating these products do not always have a software engineering background. To ensure quality, tools are needed that automate and support software testing while allowing these domain specialists to leverage their knowledge and experience. search-based testing could be a key aspect in creating an automated tool for supporting testing activities. However, domain specific quality criteria and trade-offs make it difficult to develop a general fitness function a priori, so interaction between domain specialists and such a tool would be critical to its success. In this paper we present a system for interactive search-based software testing and investigate a way for domain specialists to guide the search by dynamically re-weighting quality goals. Our empirical investigation shows that objective reweighting can help a human domain specialist interactively guide the search, without requiring specialized knowledge of the system and without sacrificing population diversity.

关键词： search based software testing interactive search based software engineering user centered embedded software industrial experience

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The Maturation of search-based software testing: Successes and Challenges 12

The Maturation of Search-Based Software Testing: Successes a...

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IEEE/ACM 12th International Workshop on search-based software testing (SBST)

作者： Cohen, Myra B. Iowa State Univ Dept Comp Sci Ames IA 50011 USA

ISBN: (纸本)9781728122335

In this paper we revisit the field of search-based software testing (SBST) in the context of its technological maturity. We highlight some successes with respect to tools, hybrid approaches, extensions and industry adoption. We then discuss some open challenges that remain for SBST including the need for new approaches to system testing, automated oracle generation, incorporating humans into the search process, and leveraging learning through hyper-heuristic search.

关键词： SBST search based software testing

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Assisting in search heuristics selection through multidimensional supervised classification: A case study on software testing

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INFORMATION SCIENCES 2014年 258卷 122-139页

作者： Sagarna, Ramon Mendiburu, Alexander Inza, Inaki Lozano, Jose A. Univ Basque Country Intelligent Syst Grp San Sebastian 20018 Spain

A fundamental question in the field of approximation algorithms, for a given problem instance, is the selection of the best (or a suitable) algorithm with regard to some performance criteria. A practical strategy for facing this problem is the application of machine learning techniques. However, limited support has been given in the literature to the case of more than one performance criteria, which is the natural scenario for approximation algorithms. We propose multidimensional Bayesian network (mBN) classifiers as a relatively simple, yet well-principled, approach for helping to solve this problem. Precisely, we relax the algorithm selection decision problem into the elucidation of the nondominated subset of algorithms, which contains the best. This formulation can be used in different ways to elucidate the main problem, each of which can be tackled with an mBN classifier. Namely, we deal with two of them: the prediction of the whole nondominated set and whether an algorithm is nondominated or not. We illustrate the feasibility of the approach for real-life scenarios with a case study in the context of search based software Test Data Generation (SBSTDG). A set of five SBSTDG generators is considered and the aim is to assist a hypothetical test engineer in elucidating good generators to fulfil the branch testing of a given programme. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Algorithm selection Multidimensional classification Dominance relation search based software testing

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Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies

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IEEE TRANSACTIONS ON software ENGINEERING 2022年第3期48卷 803-818页

作者： Guizzo, Giovani Sarro, Federica Krinke, Jens Vergilio, Silvia R. UCL Gower St London WC1E 6EA England Univ Fed Parana BR-82590300 Curitiba Parana Brazil

Mutation testing is an effective approach to evaluate and strengthen software test suites, but its adoption is currently limited by the mutants' execution computational cost. Several strategies have been proposed to reduce this cost (a.k.a. mutation cost reduction strategies), however none of them has proven to be effective for all scenarios since they often need an ad-hoc manual selection and configuration depending on the software under test (SUT). In this paper, we propose a novel multi-objective evolutionary hyper-heuristic approach, dubbed Sentinel, to automate the generation of optimal cost reduction strategies for every new SUT. We evaluate Sentinel by carrying out a thorough empirical study involving 40 releases of 10 open-source real-world software systems and both baseline and state-of-the-art strategies as a benchmark. We execute a total of 4,800 experiments, and evaluate their results with both quality indicators and statistical significance tests, following the most recent best practice in the literature. The results show that strategies generated by Sentinel outperform the baseline strategies in 95 percent of the cases always with large effect sizes. They also obtain statistically significantly better results than state-of-the-art strategies in 88 percent of the cases, with large effect sizes for 95 percent of them. Also, our study reveals that the mutation strategies generated by Sentinel for a given software version can be used without any loss in quality for subsequently developed versions in 95 percent of the cases. These results show that Sentinel is able to automatically generate mutation strategies that reduce mutation testing cost without affecting its testing effectiveness (i.e., mutation score), thus taking off from the tester's shoulders the burden of manually selecting and configuring strategies for each SUT.

关键词： testing Maintenance engineering Computational efficiency Open source software software engineering search problems Mutation testing mutant reduction software testing grammatical evolution hyper-heuristic search based software testing search based software engineering

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A systematic review on search based mutation testing

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INFORMATION AND software TECHNOLOGY 2017年 81卷 19-35页

作者： Silva, Rodolfo Adamshuk Senger de Souza, Simone do Rocio Lopes de Souza, Paulo Sergio Univ Sao Paulo Inst Math & Comp Sci BR-13566590 Sao Carlos SP Brazil

Context: search based software testing refers to the use of meta-heuristics for the optimization of a task in the context of software testing. Meta-heuristics can solve complex problems in which an optimum solution must be found among a large amount of possibilities. The use of meta-heuristics in testing activities is promising because of the high number of inputs that should be tested. Previous studies on search based software testing have focused on the application of meta-heuristics for the optimization of structural and functional criteria. Recently, some researchers have proposed the use of SBST for mutation testing and explored solutions for the cost of application of this testing criterion. Objective: The objective is to identify how SBST has been explored in the context of mutation testing, how fitness functions are defined and the challenges and research opportunities in the application of meta-heuristic search techniques. Method: A systematic review involving 263 papers published between 1996 and 2014 examined the studies on the use of meta-heuristic search techniques for the optimization of mutation testing. Results: The results show meta-heuristic search techniques have been applied for the optimization of test data generation, mutant generation and selection of effective mutation operators. Five meta-heuristic techniques, namely Genetic Algorithm, Ant Colony, Bacteriological Algorithm, Hill Climbing and Simulated Annealing have been used in search based mutation testing. The review addressed different fitness functions used to guide the search. Conclusion: search based mutation testing is a field of interest, however, some issues remain unexplored. For instance, the use of meta-heuristics for the selection of effective mutation operators was identified in only one study. The results have pointed a range of possibilities for new studies to be developed, i.e., identification of equivalent mutants, experimental studies and application to different domains,

关键词： Mutation testing search based software testing Meta-heuristic

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Instance Generator and Problem Representation to Improve Object Oriented Code Coverage

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IEEE TRANSACTIONS ON software ENGINEERING 2015年第3期41卷 294-313页

作者： Sakti, Abdelilah Pesant, Gilles Gueheneuc, Yann-Gael Ecole Polytech Dept Comp & Software Engn Montreal PQ H3C 3A7 Canada

search-based approaches have been extensively applied to solve the problem of software test-data generation. Yet, test-data generation for object-oriented programming (OOP) is challenging due to the features of OOP, e.g., abstraction, encapsulation, and visibility that prevent direct access to some parts of the source code. To address this problem we present a new automated search-based software test-data generation approach that achieves high code coverage for unit-class testing. We first describe how we structure the test-data generation problem for unit-class testing to generate relevant sequences of method calls. Through a static analysis, we consider only methods or constructors changing the state of the class-under-test or that may reach a test target. Then we introduce a generator of instances of classes that is based on a family of means-of-instantiation including subclasses and external factory methods. It also uses a seeding strategy and a diversification strategy to increase the likelihood to reach a test target. Using a search heuristic to reach all test targets at the same time, we implement our approach in a tool, JTExpert, that we evaluate on more than a hundred Java classes from different open-source libraries. JTExpert gives better results in terms of search time and code coverage than the state of the art, EvoSuite, which uses traditional techniques.

关键词： Automatic test data generation search based software testing unit class testing seeding strategy diversification strategy Java testing

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An accelerating PSO algorithm based test data generator for data-flow dependencies using dominance concepts

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2017年第2期8卷 1534-1552页

作者： Kumar, Sumit Yadav, D. K. Khan, D. A. KIET Ghaziabad India NIT Jamshedpur Bihar India

One of the most important and effort intensive activity of the entire software development process is software testing. The effort involved chiefly increases because of the need to obtain optimal test data out of the entire search space of the problem under testing. software test data generation is one area that has seen tremendous research in terms of automation and optimization. Generating or identifying an optimal test set that satisfies a more robust adequacy criteria, like data flow testing, is still a challenging task. A number of heuristic and meta-heuristics like GA, PSO have been applied to optimize the test data generation problem. GA, although more popular, has its own difficulties such as complex to implement and slow convergence rate. In this paper an accelerating particle swarm optimization algorithm (APSO) is applied to generate test data for data-flow dependencies of a program guided by a new fitness function. APSO is used because of its capability of balancing in exploration and exploitation. A new fitness function is designed based on the concepts of dominance relations, weighted branch distance for APSO to guide the search direction. A set of benchmark programs and four modules of Krishna Institute of Engineering and Technology ERP system were taken for the experimental analysis. The experimental results show that the proposed APSO based approach performed significantly better than random search, genetic algorithm and PSO in enhancing the convergence speed.

关键词： search based software testing Evolutionary algorithms Particle swarm optimization Data flow testing Dominance tree

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