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

共 143 条记录，以下是111-120 订阅

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Deep Learning Library testing via Effective Model Generation 2020

Deep Learning Library Testing via Effective Model Generation

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28th ACM Joint Meeting on European software Engineering Conference and Symposium on the Foundations of software Engineering (ESEC/FSE)

作者： Wang, Zan Yan, Ming Chen, Junjie Liu, Shuang Zhang, Dongdi Tianjin Univ Coll Intelligence & Comp Shanghai Peoples R China State Key Lab Commun Content Cognit China Beijing Peoples R China

ISBN: (纸本)9781450370431

Deep learning (DL) techniques are rapidly developed and have been widely adopted in practice. However, similar to traditional software, DL systems also contain bugs, which could cause serious impacts especially in safety-critical domains. Recently, much research has focused on testing DL models, while little attention has been paid for testing DL libraries, which is the basis of building DL models and directly affects the behavior of DL systems. In this work, we propose a novel approach, LEMON, to testing DL libraries. In particular, we (1) design a series of mutation rules for DL models, with the purpose of exploring different invoking sequences of library code and hard-to-trigger behaviors;and (2) propose a heuristic strategy to guide the model generation process towards the direction of amplifying the inconsistent degrees of the inconsistencies between different DL libraries caused by bugs, so as to mitigate the impact of potential noise introduced by uncertain factors in DL libraries. We conducted an empirical study to evaluate the effectiveness of LEMON with 20 release versions of 4 widely-used DL libraries, i.e., TensorFlow, Theano, CNTK, MXNet. The results demonstrate that LEMON detected 24 new bugs in the latest release versions of these libraries, where 7 bugs have been confirmed and one bug has been fixed by developers. Besides, the results confirm that the heuristic strategy for model generation indeed effectively guides LEMON in amplifying the inconsistent degrees for bugs.

关键词： Deep Learning testing Library testing Model Generation Mutation search-based software testing

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testing Autonomous Cars for Feature Interaction Failures using Many-Objective search 18

Testing Autonomous Cars for Feature Interaction Failures usi...

引用

33rd IEEE/ACM International Conference on Automated software Engineering (ASE)

作者： Ben Abdessalem, Raja Panichella, Annibale Nejati, Shiva Briand, Lionel C. Stifter, Thomas Univ Luxembourg Luxembourg Luxembourg IEE SA Luxembourg Luxembourg

ISBN: (数字)9781450359375

ISBN: (纸本)9781450359375

Complex systems such as autonomous cars are typically built as a composition of features that are independent units of functionality. Features tend to interact and impact one another's behavior in unknown ways. A challenge is to detect and manage feature interactions, in particular, those that violate system requirements, hence leading to failures. In this paper, we propose a technique to detect feature interaction failures by casting this problem into a search-based test generation problem. We define a set of hybrid test objectives (distance functions) that combine traditional coverage based heuristics with new heuristics specifically aimed at revealing feature interaction failures. We develop a new search-based test generation algorithm, called FITEST, that is guided by our hybrid test objectives. FITEST extends recently proposed many-objective evolutionary algorithms to reduce the time required to compute fitness values. We evaluate our approach using two versions of an industrial self-driving system. Our results show that our hybrid test objectives are able to identify more than twice as many feature interaction failures as two baseline test objectives used in the software testing literature (i.e., coverage-based and failure-based test objectives). Further, the feedback from domain experts indicates that the detected feature interaction failures represent real faults in their systems that were not previously identified based on analysis of the system features and their requirements.

关键词： search-based software testing Many-Objective Optimization Automotive Systems Feature Interaction Problem

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A Path and Branch based Approach to Fitness Computation for Program Test Data Generation using Genetic Algorithm 1

A Path and Branch Based Approach to Fitness Computation for ...

引用

International Conference On Futuristic Trends in Computational Analysis and Knowledge Management

作者： Pachauri, Ankur Gursaran Mishra, Gaurav Rajiv Acad Technol & Management Mathura India Dayalbagh Educ Inst Fac Sci Agra Uttar Pradesh India Amity Univ Dept Appl Sci Greater Noida India

ISBN: (纸本)9781479984336

In this paper we present a novel approach for fitness computation for test data generation using genetic algorithm. Fitness computation is a two-step process. In the first step a target node sequence is determined and in the second step the actual execution path is compared with the target node sequence to compute fitness. Fitness computation uses both branch and path information. Experiments indicate that the described fitness technique results in significant improvement in search performance

关键词： program test data generation search-based software testing genetic algorithm

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Modelling and search-based testing of Robot Controllers Using Enzymatic Numerical P Systems 7

Modelling and Search-Based Testing of Robot Controllers Usin...

引用

7th Symposium on Working Formal Methods (FROM)

作者： Bobe, Radu Traian Ipate, Florentin Niculescu, Ionut Mihai Univ Bucharest Fac Math & Comp Sci Dept Comp Sci Str Acad 14 Bucharest 010014 Romania

The safety of the systems controlled by software is a very important area in a digitalized society, as the number of automated processes is increasing. In this paper, we present the results of testing the accuracy of different lane keeping controllers for an educational robot. In our approach, the robot is controlled using numerical P systems and enzymatic numerical P systems. For tests generation, we used an open-source tool implementing a search-based software testing approach.

关键词： tests generation numerical P systems enzymatic numerical P systems search-based software testing cyber-physical systems membrane computing

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Well-informed Test Case Generation and Crash Reproduction 13

Well-informed Test Case Generation and Crash Reproduction

引用

13th IEEE International Conference on software testing, Verification and Validation (ICST)

作者： Derakhshanfar, Pouria Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781728157788

search-based test data generation approaches have come a long way over the past few years, but these approaches still have some limitations when it comes to exercising specific behavior for triggering particular kinds of faults (e.g., crashes or specific types of integration between classes/modules). In this thesis, we are investigating new fitness functions and evolutionary-based algorithms and techniques to tackle these limitations. We have defined multiple novel approaches for crash reproduction and class integration testing. Currently, we are still working on improving both crash reproduction and class integration testing.

关键词： search-based software testing automated crash reproduction automated integration testing

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Evolutionary testing for Crash Reproduction 9

Evolutionary Testing for Crash Reproduction

引用

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

作者： Soltani, Mozhan Panichella, Annibale van Deursen, Arie Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781450341660

Manual crash reproduction is a labor-intensive and time-consuming task. Therefore, several solutions have been proposed in literature for automatic crash reproduction, including generating unit tests via symbolic execution and mutation analysis. However, various limitations adversely affect the capabilities of the existing solutions in covering a wider range of crashes because generating helpful tests that trigger specific execution paths is particularly challenging. In this paper, we propose a new solution for automatic crash reproduction based on evolutionary unit test generation techniques. The proposed solution exploits crash data from collected stack traces to guide search-based algorithms toward the generation of unit test cases that can reproduce the original crashes. Results from our preliminary study on real crashes from Apache Commons libraries show that our solution can successfully reproduce crashes which are not reproducible by two other state-of-art techniques.

关键词： Crash Reproduction Genetic Algorithm search-based software testing Test Case Generation

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The Evolutionary Landscape of SBST: a 10 Year Perspective 10

The Evolutionary Landscape of SBST: a 10 Year Perspective

引用

IEEE/ACM 10th International Workshop on search-based software testing (SBST)

作者： Cohen, Myra B. Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA

ISBN: (纸本)9781538627891

A key indicator of the health and quality of any evolutionary algorithm is the landscape of its search. By analyzing the landscape one can determine the peaks (local maxima) where significant solutions exist. In this paper we examine the landscape for the history of the International Workshop on search-based software testing (SBST) within the context of the broader field of search-based software testing. We study the evolution of the field, highlighting key advances during three phases of its ten year history. In 2008 the focus of SBST was inner looking, with advances in existing search techniques, improvements to individual generation techniques, and methods to transform the problem space for search effectiveness. However, diverse seeds of new ideas (such as automated program repair) were already being injected into the population. A few SBST tools existed, but the engineer still required skill and expertise to effectively apply search based approaches. During the middle years, open source tools were created and released, whole test suite generation appeared, and searches hybridized. Tool competitions began and industry started to play a stronger role. As we move to the most recent workshop years and look towards the future, more sophisticated techniques such as those that incorporate hyperheuristics via learning, and/or balance multiple objectives at once are now common. SBST has become a mainstream topic in the testing community, tools are being commercialized and these tools often hide their inner workings, leading to a future that is optimized towards SBST for all.

关键词： search-based software testing SBST

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Bio-Inspired Optimization of Test Data Generation for Concurrent software 11th

Bio-Inspired Optimization of Test Data Generation for Concur...

引用

11th International Symposium on search-based software Engineering (SSBSE)

作者： Vilela, Ricardo F. Pinto, Victor H. S. C. Colanzi, Thelma E. Souza, Simone R. S. Univ Sao Paulo ICMC USP Inst Math & Comp Sci Trabalhador Sao Carlense Ave 400 BR-13566590 Sao Carlos SP Brazil Univ Estadual Maringa UEM Informat Dept Av Colombo 5790Zona 7 BR-87020900 Maringa Parana Brazil

ISBN: (纸本)9783030274559;9783030274542

Concurrent software includes a number of key features such as communication, concurrency, and non-determinism, which increase the complexity of software testing. One of the main challenges is the test data generation. Techniques of search-based software can also benefit concurrent software testing. To do so, this paper adopts a bio-inspired approach, called BioConcST, to support the automatic test data generation for concurrent programs. BioConcST uses a Genetic Algorithm (GA) and an evolutionary strategy adapted to accept genetic information from some bad individuals (test data) in order to generate better individuals. Structural testing criteria for concurrent programs are used to guide the evolution of test data generation. An experimental study was carried out to compare BioConcST with an elitist GA strategy (EGA) in terms of adequacy of testing criteria for message-passing and share-dmemory programs. Twelve concurrent Java programs were included and the results suggest BioConcST is a promising approach, since in all the testing criteria evaluated, it achieved a better coverage and the effect-size measure was large in most cases.

关键词： Concurrent software testing Structural testing search-based software testing Genetic algorithm Test data generation

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Summary of search-based Crash Reproduction using Behavioral Model Seeding 14

Summary of Search-based Crash Reproduction using Behavioral ...

引用

14th IEEE Conference on software testing, Verification and Validation (ICST)

作者： Derakhshanfar, Pouria Devroey, Xavier Perrouin, Gilles Zaidman, Andy van Deursen, Arie Delft Univ Technol Delft Netherlands Univ Namur NADI PReCISE Namur Belgium

This is an extended abstract of the article: Pouria Derakhshanfar, Xavier Devroey, Gilles Perrouin, Andy Zaidman and Arie van Deursen. 2019. search-based crash reproduction using behavioural model seeding. In: Softwar... 详细信息

ISBN: (纸本)9781728168364

关键词： model seeding seed learning crash reproduction search-based software testing

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SimCoTest: A Test Suite Generation Tool for Simulink/Stateflow Controllers 16

SimCoTest: A Test Suite Generation Tool for Simulink/Statefl...

引用

38th IEEE/ACM International Conference on software Engineering Companion (ICSE)

作者： Matinnejad, Reza Nejati, Shiva Briand, Lionel C. Bruckmann, Thomas Univ Luxembourg SnT Ctr Luxembourg Luxembourg Delphi Automot Syst Luxembourg Luxembourg

ISBN: (纸本)9781450342056

We present SimCoTest, a tool to generate small test suites with high fault revealing ability for Simulink/Stateflow controllers. SimCoTest uses meta-heuristic search to (1) maximize the likelihood of presence of specific failure patterns in output signals (failure-based test generation), and to (2) maximize diversity of output signal shapes (output diversity test generation). SimCoTest has been evaluated on industrial Simulink models and has been systematically compared with Simuilnk Design Verifier (SLDV), an alternative commercial Simulink testing tool. Our results show that the fault revealing ability of SimCoTest outperforms that of SLDV. Further, in contrast to SLDV, SimCoTest is applicable to Simulink/Stateflow models in their entirety. A video describing the main features of SimCoTest is available at: https://***/YnXgveiGXEA

关键词： Simulink/Stateflow models software testing search-based software testing Output diversity Failure-based test generation Simulink Design Verifier (SLDV)

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