A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation

Ali, Shaukat; Briand, Lionel C.; Hemmati, Hadi; Panesar-Walawege, Rajwinder Kaur

doi:10.1109/tse.2009.52

Cited by 362 publications

(257 citation statements)

References 41 publications

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“…This has proved to be a widely applicable and successful approach, with applications from requirements and design [15], [16] to maintenance and testing [17], [18], [19]. Computational search has been exploited by all engineering disciplines, not just Software Engineering.…”

Section: When Does Ai For Se Work Well?mentioning

confidence: 99%

The role of Artificial Intelligence in Software Engineering

Harman

2012

2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE)

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Abstract-There has been a recent surge in interest in the application of Artificial Intelligence (AI) techniques to Software Engineering (SE) problems. The work is typified by recent advances in Search Based Software Engineering, but also by long established work in Probabilistic reasoning and machine learning for Software Engineering. This paper explores some of the relationships between these strands of closely related work, arguing that they have much in common and sets out some future challenges in the area of AI for SE.

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Section: When Does Ai For Se Work Well?mentioning

confidence: 99%

The role of Artificial Intelligence in Software Engineering

Harman

2012

2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE)

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“…We report an updated literature analysis from which we observe that approximately half of all SBSE papers are SBST papers, a figure little changed since the last thorough publication audit (for papers up to 2009), which found 54% of SBSE papers concerned SBST [56]. Many excellent and detailed surveys of the SBST literature can be found elsewhere [2], [4], [55], [85], [126]. Therefore, rather than attempting another survey, we provide an analysis of SBST research trends, focusing on open challenges and areas for future work and development.…”

Section: Introductionmentioning

confidence: 92%

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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“…Those papers that present results on SBSE generally (though not exclusively) fall in the category of empirical software engineering papers, for which the systematic review of Ali et al [4] sets out useful guidelines relevant to SBSE.…”

Section: Next Steps: Getting Startedmentioning

confidence: 99%

“…For example, there are surveys and overviews, covering SBSE for requirements [111], design [78] and testing [3,4,65], as well as general surveys of the whole field of SBSE [21,36,48].…”

Section: Introductionmentioning

confidence: 99%

Search Based Software Engineering: Techniques, Taxonomy, Tutorial

Harman

McMinn

Souza

et al. 2012

Empirical Software Engineering and Verification

224

257

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Abstract. The aim of Search Based Software Engineering (SBSE) research is to move software engineering problems from human-based search to machine-based search, using a variety of techniques from the metaheuristic search, operations research and evolutionary computation paradigms. The idea is to exploit humans' creativity and machines' tenacity and reliability, rather than requiring humans to perform the more tedious, error prone and thereby costly aspects of the engineering process. SBSE can also provide insights and decision support. This tutorial will present the reader with a step-by-step guide to the application of SBSE techniques to Software Engineering. It assumes neither previous knowledge nor experience with Search Based Optimisation. The intention is that the tutorial will cover sufficient material to allow the reader to become productive in successfully applying search based optimisation to a chosen Software Engineering problem of interest. IntroductionSearch Based Software Engineering (SBSE) is the name given to a body of work in which Search Based Optimisation is applied to Software Engineering. This approach to Software Engineering has proved to be very successful and generic. It has been a subfield of software engineering for ten years [45], the past five of which have been characterised by an explosion of interest and activity [48]. New application areas within Software Engineering continue to emerge and a body of empirical evidence has now accrued that demonstrates that the search based approach is definitely here to stay. SBSE seeks to reformulate Software Engineering problems as 'search problems' [45,48]. This is not to be confused with textual or hypertextual searching. Rather, for Search Based Software Engineering, a search problem is one in which optimal or near optimal solutions are sought in a search space of candidate solutions, guided by a fitness function that distinguishes between better and worse solutions. The term SBSE was coined by Harman and Jones [45] in 2001, which was the first paper to advocate Search Based Optimisation as a general approach to Software Engineering, though there were other authors who had previously applied search based optimisation to aspects of Software Engineering.SBSE has been applied to many fields within the general area of Software Engineering, some of which are already sufficiently mature to warrant their own surveys. For example, there are surveys and overviews, covering SBSE for requirements [111], design [78] and testing [3,4,65], as well as general surveys of the whole field of SBSE [21,36,48]. This paper does not seek to duplicate these surveys, though some material is repeated from them (with permission), where it is relevant and appropriate. Rather, this paper aims to provide those unfamiliar with SBSE with a tutorial and practical guide. The aim is that, having read this paper, the reader will be able to begin to develop SBSE solutions to a chosen software engineering problem and will be able to collect and analyse the results of the applicati...

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A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation

Cited by 362 publications

References 41 publications

The role of Artificial Intelligence in Software Engineering

The role of Artificial Intelligence in Software Engineering

Achievements, Open Problems and Challenges for Search Based Software Testing

Search Based Software Engineering: Techniques, Taxonomy, Tutorial

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