A systematic review on search based mutation testing

Silva, Rodolfo Adamshuk; Souza, Simone do Rocio Senger de; Souza, Paulo Sérgio Lopes de

doi:10.1016/j.infsof.2016.01.017

Cited by 67 publications

(40 citation statements)

References 33 publications

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“…Silva et al [15] surveyed the studies applying search based techniques in the scope of mutation testing. However, most of these works are devoted to test data generation, even for object-oriented software [9], and only a few to mutant generation (where EMT is classified).…”

Section: Related Workmentioning

confidence: 99%

GiGAn

Delgado‐Pérez

Medina‐Bulo

Segura

et al. 2017

Proceedings of the Symposium on Applied Computing

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The current trend in mutation testing is to reduce the great testing effort that it involves, but it should be based on wellstudied cost reduction techniques. Evolutionary Mutation Testing (EMT) aims at generating a reduced set of mutants by means of an evolutionary algorithm, which searches for potentially equivalent and difficult to kill mutants to help improve the test suite. However, there is little evidence of its applicability to other contexts beyond WS-BPEL compositions. This study explores its performance when applied to C++ object-oriented programs thanks to a newly developed system, GiGAn. The conducted experiments reveal that EMT shows stable behavior in all the case studies, where the best results are obtained when a low percentage of the mutants is generated. They also support previous studies of EMT when compared to random mutant selection, reinforcing its use for the goal of improving the fault detection capability of the test suite. CCS Concepts•Software and its engineering → Software testing and debugging; Search-based software engineering;

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Section: Related Workmentioning

confidence: 99%

GiGAn

Delgado‐Pérez

Medina‐Bulo

Segura

et al. 2017

Proceedings of the Symposium on Applied Computing

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“…Over the years SBSE has been applied to various fields of software engineering for example, requirements [16,109], automatic program repair [52], Software Product Lines [15,31,83], Performance configuration optimization [32,66,67,69,72] to name of few. SBSE has been applied to other fields and has their own surveys such as design [79], model-driven engineering [12], genetic improvement of programs [76], refactoring [56], Testing [46,86] as well as more general surveys [18,34]. Figure 2 provides a (very) short tutorial on SBSE and Figure 3 characterizes some of the differences between MSR and SBSE.…”

Section: What? (Definitions)mentioning

confidence: 99%

Data-driven search-based software engineering

Nair

Agrawal

Chen

et al. 2018

Proceedings of the 15th International Conference on Mining Software Repositories

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This paper introduces Data-Driven Search-based Software Engineering (DSE), which combines insights from Mining Software Repositories (MSR) and Search-based Software Engineering (SBSE). While MSR formulates software engineering problems as data mining problems, SBSE reformulate Software Engineering (SE) problems as optimization problems and use meta-heuristic algorithms to solve them. Both MSR and SBSE share the common goal of providing insights to improve software engineering. The algorithms used in these two areas also have intrinsic relationships. We, therefore, argue that combining these two fields is useful for situations (a) which require learning from a large data source or (b) when optimizers need to know the lay of the land to find better solutions, faster.This paper aims to answer the following three questions: (1) What are the various topics addressed by DSE?, (2) What types of data are used by the researchers in this area?, and (3) What research approaches do researchers use? The paper briefly sets out to act as a practical guide to develop new DSE techniques and also to serve as a teaching resource.This paper also presents a resource (tiny.cc/data-se) for exploring DSE. The resource contains 89 artifacts which are related to DSE, divided into 13 groups such as requirements engineering, software product lines, software processes. All the materials in this repository have been used in recent software engineering papers; i.e., for all this material, there exist baseline results against which researchers can comparatively assess their new ideas.

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“…We use search-based method to solve the multi-objective optimization. Like other search-based software engineering tasks [4,7,10,13,20], MOOSE contains representation, fitness function, and computational search algorithm. It also contains a fourth part to illustrate how to handle multiple objectives.…”

Section: Moosementioning

confidence: 99%

“…In SBSE, many software engineering problems are reformulated as search problems, such as test case selection [4,10,20], and mutation testing [7,13].…”

Section: Rq2: Is Each Of the Three Objectives Necessary In Moose?mentioning

confidence: 99%

Multi-Objective Crowd Worker Selection in Crowdsourced Testing

Cui

Wang

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Crowdsourced testing is an emerging trend in software testing, which relies on crowd workers to accomplish test tasks. Typically, a crowdsourced testing task aims to detect as many bugs as possible within a limited budget. For a specific test task, not all crowd workers are qualified to perform it, and different test tasks require crowd workers to have different experiences, domain knowledge, etc. Inappropriate workers may miss true bugs, introduce false bugs, or report duplicated bugs, which could not only decrease the quality of test outcomes, but also increase the cost of hiring workers. Thus, how to select the appropriate crowd workers for specific test tasks is a challenge in crowdsourced testing.This paper proposes a Multi-Objective crowd wOrker SElection approach (MOOSE), which includes three objectives: maximizing the coverage of test requirement, minimizing the cost, and maximizing bug-detection experience of the selected crowd workers. Specifically, MOOSE leverages NSGA-II, a widely used multi-objective evolutionary algorithm, to optimize the three objectives when selecting workers. We evaluate MOOSE on 42 test tasks (involve 844 crowd workers and 3,984 test reports) from one of the largest crowdsourced testing platforms in China, and the experimental results show MOOSE could improve the best baseline by 17% on average in bug detection rate.

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A systematic review on search based mutation testing

Cited by 67 publications

References 33 publications

GiGAn

GiGAn

Data-driven search-based software engineering

Multi-Objective Crowd Worker Selection in Crowdsourced Testing

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