Search based software engineering for software product line engineering

Harman, Mark; Jia, Yunyi; Krinke, Jens; Langdon, William B.; Petke, Justyna; Zhang, Y.

doi:10.1145/2648511.2648513

Cited by 113 publications

(73 citation statements)

References 168 publications

(166 reference statements)

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“…For instance, Davril et al apply feature mining and feature associations mining to informal product descriptions [5]. There exist several papers that apply search based techniques, which generally give better results [10,17,6,16]. However, checking and maintaining the consistency between a SPL and its feature model is still an open problem.…”

Section: Related Workmentioning

confidence: 99%

Validation of Constraints Among Configuration Parameters Using Search-Based Combinatorial Interaction Testing

Gargantini

Petke

Radavelli

et al. 2016

Search Based Software Engineering

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Abstract. The appeal of highly-configurable software systems lies in their adaptability to users' needs. Search-based Combinatorial Interaction Testing (CIT) techniques have been specifically developed to drive the systematic testing of such highly-configurable systems. In order to apply these, it is paramount to devise a model of parameter configurations which conforms to the software implementation. This is a non-trivial task. Therefore, we extend traditional search-based CIT by devising 4 new testing policies able to check if the model correctly identifies constraints among the various software parameters. Our experiments show that one of our new policies is able to detect faults both in the model and the software implementation that are missed by the standard approaches.

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Section: Related Workmentioning

confidence: 99%

Validation of Constraints Among Configuration Parameters Using Search-Based Combinatorial Interaction Testing

Gargantini

Petke

Radavelli

et al. 2016

Search Based Software Engineering

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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)

158

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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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“…A more detailed comparison on the obtained feature models with both approaches is also part of our future work. Harman et al (2014) performed a survey on the topic of SBSE applied to SPLs. They present an overview of recent articles classified according to themes such as configuration, testing, or architectural improvement.…”

Section: Related Workmentioning

confidence: 99%

An assessment of search-based techniques for reverse engineering feature models

Lopez-Herrejon

Linsbauer

Galindo

et al. 2015

Journal of Systems and Software

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Successful software evolves from a single system by adding and changing functionality to keep up with users' demands and to cater to their similar and different requirements. Nowadays it is a common practice to offer a system in many variants such as community, professional, or academic editions. Each variant provides different functionality described in terms of features. Software Product Line Engineering (SPLE) is an effective software development paradigm for this scenario. At the core of SPLE is variability modelling whose goal is to represent the combinations of features that distinguish the system variants using feature models, the de facto standard for such task. As SPLE practices are becoming more pervasive, reverse engineering feature models from the feature descriptions of each individual variant has become an active research subject. In this paper we evaluated, for this reverse engineering task, three standard search based techniques (evolutionary algorithms, hill climbing, and random search) with two objective functions on 74 SPLs. We compared their performance using precision and recall, and found a clear trade-off between these two metrics which we further reified into a third objective function based on F β , an information retrieval measure, that showed a clear performance improvement. We believe that this work sheds light on the great potential of search-based techniques for SPLE tasks.

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Search based software engineering for software product line engineering

Cited by 113 publications

References 168 publications

Validation of Constraints Among Configuration Parameters Using Search-Based Combinatorial Interaction Testing

Validation of Constraints Among Configuration Parameters Using Search-Based Combinatorial Interaction Testing

Achievements, Open Problems and Challenges for Search Based Software Testing

An assessment of search-based techniques for reverse engineering feature models

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