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检索条件"主题词=Search-based Software Testing"

共 143 条记录，以下是91-100 订阅

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Deeper at the SBST 2021 Tool Competition: ADAS testing Using Multi-Objective search 14

Deeper at the SBST 2021 Tool Competition: ADAS Testing Using...

引用

14th IEEE/ACM International Workshop on search-based software testing (SBST)

作者： Moghadam, Mahshid Helali Borg, Markus Mousavirad, Seyed Jalaleddin RISE Res Inst Sweden Gothenburg Sweden Malardalen Univ Vasteras Sweden Hakim Sabzevari Univ Dept Comp Engn Sabzevar Iran

ISBN: (纸本)9781665445719

Deeper is a simulation-based test generator that uses an evolutionary process, i.e., an archive-based NSGA-II augmented with a quality population seed, for generating test cases to test a deep neural network-based lane-keeping system. This paper presents Deeper briefly and summarizes the results of Deeper's participation in the Cyber-physical systems (CPS) testing competition at SBST 2021.

关键词： search-based software testing cyber-physical systems advanced driver assistance systems deep learning automotive simulators

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Adding Contextual Guidance to the Automated search for Probabilistic Test Profiles

Adding Contextual Guidance to the Automated Search for Proba...

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7th IEEE International Conference on software testing, Verification and Validation (ICST)

作者： Poulding, Simon Waeselynck, Helene Univ York Dept Comp Sci York YO10 5DD N Yorkshire England Univ Toulouse LAAS CNRS Toulouse France

ISBN: (纸本)9780769551852

Statistical testing is a probabilistic approach to test data generation that has been demonstrated to be very effective at revealing faults. Its premise is to compensate for the imperfect connection between coverage criteria and the faults to be revealed by exercising each coverage element several times with different random data. The cornerstone of the approach is the often complex task of determining a suitable input profile, and recent work has shown that automated metaheuristic search can be a practical method of synthesising such profiles. The starting point of this paper is the hypothesis that, for some software, the existing grammar-based representation used by the search algorithm fails to capture important relationships between input arguments and this can limit the fault-revealing power of the synthesised profiles. We provide evidence in support of this hypothesis, and propose a solution in which the user provides some basic contextual knowledge to guide the search. Empirical results for two case studies are promising: knowledge gained by a very straightforward review of the software-undertest is sufficient to dramatically increase the efficacy of the profiles synthesised by search.

关键词： grammar-based testing search-based software testing statistical testing

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Syntest-JavaScript: Automated Unit-Level Test Case Generation for JavaScript 17

Syntest-JavaScript: Automated Unit-Level Test Case Generatio...

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17th IEEE/ACM International Workshop on search-based and Fuzz testing (SBFT)

作者： Olsthoorn, Mitchell Stallenberg, Dimitri Panichella, Annibale Delft Univ Technol Delft Netherlands

ISBN: (纸本)9798400705625

Over the last decades, various tools (e.g., AUSTIN and EvoSuite) have been developed to automate the process of unit-level test case generation. Most of these tools are designed for statically-typed languages, such as C and Java. However, as is shown in recent Stack Overflow developer surveys, the popularity of dynamically-typed languages, such as JavaScript and Python, has been increasing and is dominating the charts. Only recently, tools for automated test case generation of dynamically-typed languages have started to emerge ( e.g., Pynguin for Python). However, to the best of our knowledge, there is no tool that focuses on automated test case generation for server-side JavaScript. To this aim, we introduce SynTest-JavaScript, a user-friendly tool for automated unit-level test case generation for (server-side) JavaScript. To showcase the effectiveness of SynTest-JavaScript, we empirically evaluate it on five large open-source JavaScript projects and one artificial one.

关键词： software testing search-based software testing test case generation fuzzing javascript syntest

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Multi-Objective White-Box Test Input Selection for Deep Neural Network Model Enhancement 34

Multi-Objective White-Box Test Input Selection for Deep Neur...

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34th IEEE International Symposium on software Reliability Engineering (ISSRE)

作者： Guo, Hongjing Tao, Chuanqi Huang, Zhiqiu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9798350315943

To reveal incorrect behaviors and improve the quality of DNN models through testing, a commonly used approach is to collect massive test inputs and manually label them for model optimization. However, it is labor-intensive and time-consuming to manually label the whole collected test inputs. To reduce the labeling cost, recent studies propose test input selection approaches to select a subset of test inputs for model retraining, thereby improving the performance of DNN models. Existing approaches primarily rely on output probabilities to select error-revealing test inputs. Nevertheless, such approaches select test inputs without the knowledge of underlying mechanisms that lead to incorrect decisions. Furthermore, they fail to consider the diversity of test inputs, limiting the capability of DNN models to learn more diverse features. To address these limitations, this paper proposes MOON, a white-box test input selection approach based on multi-objective optimization. The neuron spectrum is proposed to localize suspicious neurons that contribute to erroneous decisions made by DNN models. MOON formulates the test input selection method into a search-based testing problem. By tailoring a multi-objective optimization algorithm, it guides the search process towards maximizing the outputs of suspicious neurons while promoting diversity in neuron behaviors. An empirical evaluation on three datasets and six DNN models was conducted. The experimental results demonstrate the effectiveness of MOON in localizing suspicious neurons. In addition, MOON significantly outperforms state-of-the-art test selection approaches, with an accuracy improvement rate of up to 236.1%.

关键词： deep neural network testing test input selection search-based software testing model retraining

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Variable Strength Combinatorial Test Data Generation Using Enhanced Bird Swarm Algorithm 19

Variable Strength Combinatorial Test Data Generation Using E...

引用

19th IEEE/ACIS International Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Cai, Lizhi Zhang, Yang Ji, Weijia East China Univ Sci & Technol Sch Informat Sci & Engineer Shanghai Peoples R China Shanghai Dev Ctr Comp Software Technol Lab Comp Software Testing & Evaluating Shanghai Peoples R China

ISBN: (纸本)9781538658895

combinatorial testing is an effective black box testing technique for the system with large numbers of parameters and their values. However, for significantly complex and key systems, combinatorial testing still owns high complexity. testing completely the core part of these systems usually is a kind of solution and Variable strength combinatorial test data generation (VS-CTDG) emerges. In this paper, enhanced bird swarm algorithm (EBSA), a variant of Bird Swarm Algorithm (BSA), is employed into the problem. Moreover, a testing requirement reduction is proposed and makes EBSA more suitable into VS-CTDG than ever. Through benchmarks, EBSA is proved an effective approach.

关键词： search-based software testing variable strength bird swarm algorithm combinatorial testing

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Commonality-Driven Unit Test Generation 12th

Commonality-Driven Unit Test Generation

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12th International Symposium on search-based software Engineering (SSBSE)

作者： Evers, Bjorn Derakhshanfar, Pouria Devroey, Xavier Zaidman, Andy Delft Univ Technol Delft Netherlands

ISBN: (纸本)9783030597610;9783030597627

Various search-based test generation techniques have been proposed to automate the generation of unit tests fulfilling different criteria (e.g., line coverage, branch coverage, mutation score, etc.). Despite several advances made over the years, search-based unit test generation still suffers from a lack of guidance due to the limited amount of information available in the source code that, for instance, hampers the generation of complex objects. Previous studies introduced many strategies to address this issue, e.g., dynamic symbolic execution or seeding, but do not take the internal execution of the methods into account. In this paper, we introduce a novel secondary objective called commonality score, measuring how close the execution path of a test case is from reproducing a common or uncommon execution pattern observed during the operation of the software. To assess the commonality score, we implemented it in EVOSUITE and evaluated its application on 150 classes from JABREF, an open-source software for managing bibliography references. Our results are mixed. Our approach leads to test cases that indeed follow common or uncommon execution patterns. However, if the commonality score can have a positive impact on the structural coverage and mutation score of the generated test suites, it can also be detrimental in some cases.

关键词： search-based software testing Automated unit testing Common paths coverage Secondary objective

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GenRL at the SBST 2022 Tool Competition 15

GenRL at the SBST 2022 Tool Competition

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15th search-based software testing Workshop (SBST)

作者： Starace, Luigi Libero Lucio Romdhana, Andrea Di Martino, Sergio Univ Napoli Federico II Naples Italy Univ Genoa Genoa Italy FBK ICT Secur & Trust Unit Trento Italy

GenRL is a Deep Reinforcement Learning-based tool designed to generate test cases for Lane-Keeping Assist Systems. In this paper, we briefly presents GenRL, and summarize the results of its participation in the Cyber-... 详细信息

ISBN: (纸本)9781450393188

关键词： search-based software testing reinforcement learning cyber-physical systems advanced driver assistance systems automotive simulators

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Strong Mutation-based Test Data Generation using Hill Climbing 9

Strong Mutation-Based Test Data Generation using Hill Climbi...

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9th IEEE/ACM International Workshop on search-based software testing (SBST)

作者： Souza, Francisco Carlos M. Papadakis, Mike Le Traon, Yves Delamaro, Marcio E. Univ Sao Paulo Comp Syst Dept Sao Carlos SP Brazil Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust Luxembourg Luxembourg

ISBN: (纸本)9781450341660

Mutation testing is an effective test criterion for finding faults and assessing the quality of a test suite. Every test criterion requires the generation of test cases, which turns to be a manual and difficult task. In literature, search-based techniques are effective in generating structural-based test data. This fact motivates their use for mutation testing. Thus, if automatic test data generation can achieve an acceptable level of mutation score, it has the potential to greatly reduce the involved manual effort. This paper proposes an automated test generation approach, using hill climbing, for strong mutation. It incremental aims at strongly killing mutants, by focusing on mutants' propagation, i.e., how to kill mutants that are weakly killed but not strongly. Furthermore, the paper reports empirical results regarding the cost and effectiveness of the proposed approach on a set of 18 C programs. Overall, for the majority of the studied programs, the proposed approach achieved a higher strong mutation score than random testing, by 19,02% on average, and the previously proposed test generation techniques that ignore mutants' propagation, by 7,2% on average. Our results also demonstrate the improved efficiency of the proposed scheme over the previous methods.

关键词： Mutation testing search-based software testing Test Data Generation

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An Elitist Evolutionary Algorithm for Automatically Generating Test Data

An Elitist Evolutionary Algorithm for Automatically Generati...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Louzada, Jailton Camilo-Junior, Celso G. Vincenzi, Auri Rodrigues, Cassio Univ Fed Goias Inst Informat Goiania Go Brazil

ISBN: (纸本)9781467315098

The development of an effective and efficient method for generating test data is an extremely challenging process which directly impacts the time that could be spent on activities relevant to software testing. Therefore, various researches related to this area have been carried out. Among the techniques for automatically generating test data, we highlight the use of metaheuristics, a promising area called search-based software testing (SBST). Thus, this article proposes the use of an Elitist Genetic Algorithm (GA) as a tool for generation and selection of test data applied in Mutation testing for different benchmarks. The results indicate a good performance of the algorithm used in the benchmarks.

关键词： Genetic Algorithm Automatic Test Data Generating search-based software testing Mutation testing

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Automated Repair of Feature Interaction Failures in Automated Driving Systems 2020

Automated Repair of Feature Interaction Failures in Automate...

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29th ACM SIGSOFT International Symposium on software testing and Analysis (ISSTA)

作者： Ben Abdessalem, Raja Panichella, Annibale Nejati, Shiva Briand, Lionel C. Stifter, Thomas Univ Luxembourg Esch Sur Alzette Luxembourg Delft Univ Technol Delft Netherlands Univ Ottawa Ottawa ON Canada IEE SA Bissen Luxembourg

ISBN: (纸本)9781450380089

In the past years, several automated repair strategies have been proposed to fix bugs in individual software programs without any human intervention. There has been, however, little work on how automated repair techniques can resolve failures that arise at the system-level and are caused by undesired interactions among different system components or functions. Feature interaction failures are common in complex systems such as autonomous cars that are typically built as a composition of independent features (i.e., units of functionality). In this paper, we propose a repair technique to automatically resolve undesired feature interaction failures in automated driving systems (ADS) that lead to the violation of system safety requirements. Our repair strategy achieves its goal by (1) localizing faults spanning several lines of code, (2) simultaneously resolving multiple interaction failures caused by independent faults, (3) scaling repair strategies from the unit-level to the system-level, and (4) resolving failures based on their order of severity. We have evaluated our approach using two industrial ADS containing four features. Our results show that our repair strategy resolves the undesired interaction failures in these two systems in less than 16h and outperforms existing automated repair techniques.

关键词： search-based software testing Automated Driving Systems Automated software Repair Feature Interaction Problem

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