Transition coverage testing for simulink/stateflow models using messy genetic algorithms

Oh, Jungsup; Harman, Mark; Yoo, Shin

doi:10.1145/2001576.2001825

Cited by 16 publications

(13 citation statements)

References 48 publications

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“…Here is a long (yet partial) list of just some of the testing problems with citations to a few example papers (of many) that adopt an SBST approach to find suitable test data: functional testing [118], safety testing [11], [32], security testing [41], robustness testing [104], integration testing [18], [26], service-based testing [24], temporal testing [19], [113], [119], exception testing [114], Combinatorial Interaction Testing (CIT) [20], [25], [95], (and Software Product Line (SPL) testing [48]), state [77] and state-based-model testing [30], [78] (including popular modelling notations such as MATLAB Simulink [90], [129]), and mutation based test [37], [49] and mutant [65], [92] generation. The State of the Art: SBST has made many achievements, and demonstrated its wide applicability and increasing uptake.…”

Section: A Brief History Of Sbstmentioning

confidence: 99%

Achievements, Open Problems and Challenges for Search Based Software Testing

Harman¹,

Jia²,

Zhang³

2015

2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)

Self Cite

167

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Abstract-Search Based Software Testing (SBST) formulates testing as an optimisation problem, which can be attacked using computational search techniques from the field of Search Based Software Engineering (SBSE). We present an analysis of the SBST research agenda 1 , focusing on the open problems and challenges of testing non-functional properties, in particular a topic we call 'Search Based Energy Testing' (SBET), Multi-objective SBST and SBST for Test Strategy Identification. We conclude with a vision of FIFIVERIFY tools, which would automatically find faults, fix them and verify the fixes. We explain why we think such FIFIVERIFY tools constitute an exciting challenge for the SBSE community that already could be within its reach.

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Section: A Brief History Of Sbstmentioning

confidence: 99%

Achievements, Open Problems and Challenges for Search Based Software Testing

Harman¹,

Jia²,

Zhang³

2015

2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)

Self Cite

167

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“…In [30] a messy genetic algorithm (GA) is used to generate transition tours through Simulink Stateflow models. The authors identify two main challenges: trigger blocks containing timing constraints or counters and cyclic paths which may require several traversals before triggering a transition.…”

Section: Related Workmentioning

confidence: 99%

Search based algorithms for test sequence generation in functional testing

Ferrer

Kruse

Chicano

et al. 2015

Information and Software Technology

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Context: The generation of dynamic test sequences from a formal specification, complementing traditional testing methods in order to find errors in the source code. Objective: In this paper we extend one specific combinatorial test approach, the Classification Tree Method (CTM), with transition information to generate test sequences. Although we use CTM, this extension is also possible for any combinatorial testing method. Method: The generation of minimal test sequences that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search-based approaches are required to find such (near) optimal test sequences. Results: The experimental analysis compares the search-based technique with a greedy algorithm on a set of 12 hierarchical concurrent models of programs extracted from the literature. Our proposed search-based approaches (GTSG and ACOts) are able to generate test sequences by finding the shortest valid path to achieve full class (state) and transition coverage. Conclusion: The extended classification tree is useful for generating of test sequences. Moreover, the experimental analysis reveals that our search-based approaches are better than the greedy deterministic approach, especially in the most complex instances. All presented algorithms are actually integrated into a professional tool for functional testing.

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“…This method focuses on the coverage of Stateflow model elements. Five articles [34,36,40,42,43] discuss test data generation. For example, Zhan and Clark [34] present a simulation-based testing framework for automatic test data generation.…”

Section: Testingmentioning

confidence: 99%

“…tool tool chain [8,9,18,19,37] 5 10 15 20 [14, 32, 40] 25 [12] tool overview commercial tool prototype tool [4,15,20,27,28,29] [2, 3,5,6,10,11,12,13,16,21,23,24,26,31,33,34,35,36,38,39,41,42,43,44] …”

Section: Rq2: Tool Supportmentioning

confidence: 99%

Analysis and testing of matlab simulink models: a systematic mapping study

Elberzhager

Rosbach

Bauer

2013

Proceedings of the 2013 International Workshop on Joining AcadeMiA and Industry Contributions to Testing Automation

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Matlab Simulink is one of the major modeling and simulation tools applied in different embedded systems domain. Quality assurance is an essential, but often highly effort-consuming part of software development. A lot of different quality assurance techniques exist to ensure high quality, but these analysis and testing techniques are often applied in isolation. Therefore, we are interested in synergy effects when applying them in combination. Consequently, we performed a systematic mapping study to identify the current state of the art regarding such quality assurance techniques and existing combinations. Our main result is a classification of existing quality assurance techniques applied on Matlab Simulink models, and an overview of existing tool support and the validity of the approaches.

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Transition coverage testing for simulink/stateflow models using messy genetic algorithms

Cited by 16 publications

References 48 publications

Achievements, Open Problems and Challenges for Search Based Software Testing

Achievements, Open Problems and Challenges for Search Based Software Testing

Search based algorithms for test sequence generation in functional testing

Analysis and testing of matlab simulink models: a systematic mapping study

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