Proceedings ArticleDOI
Search-Based Software Testing: Past, Present and Future
Phil McMinn
- pp 153-163
TLDR
Past work and the current state of the art on Search-Based Software Testing are reviewed, and potential future research areas and open problems that remain in the field are discussed.Abstract:
Search-Based Software Testing is the use of a meta-heuristic optimizing search technique, such as a Genetic Algorithm, to automate or partially automate a testing task, for example the automatic generation of test data. Key to the optimization process is a problem-specific fitness function. The role of the fitness function is to guide the search to good solutions from a potentially infinite search space, within a practical time limit. Work on Search-Based Software Testing dates back to 1976, with interest in the area beginning to gather pace in the 1990s. More recently there has been an explosion of the amount of work. This paper reviews past work and the current state of the art, and discusses potential future research areas and open problems that remain in the field.read more
Citations
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Journal ArticleDOI
The Oracle Problem in Software Testing: A Survey
TL;DR: This paper provides a comprehensive survey of current approaches to the test oracle problem and an analysis of trends in this important area of software testing research and practice.
Journal ArticleDOI
An orchestrated survey of methodologies for automated software test case generation
Saswat Anand,Edmund K. Burke,Tsong Yueh Chen,John A. Clark,Myra B. Cohen,Wolfgang Grieskamp,Mark Harman,Mary Jean Harrold,Phil McMinn +8 more
TL;DR: An orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections, aimed at giving an introductory, up-to-date and (relatively) short overview of research in automatic test case generation.
Proceedings ArticleDOI
FairFuzz: a targeted mutation strategy for increasing greybox fuzz testing coverage
Caroline Lemieux,Koushik Sen +1 more
TL;DR: FairFuzz as discussed by the authors proposes a two-pronged approach to increase the coverage achieved by American Fuzzy Lop (AFL) by automatically identifying branches exercised by few AFL-produced inputs (rare branches).
Search Based Software Engineering: Techniques, Taxonomy, Tutorial.
TL;DR: This tutorial will present the reader with a step-by-step guide to the application of SBSE techniques to Software Engineering, and assumes neither previous knowledge nor experience with Search Based Optimisation.
Journal ArticleDOI
Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets
TL;DR: DynaMOSA is presented, a novel many-objective solver specifically designed to address the test case generation problem in the context of coverage testing and outperforms its predecessor MOSA for all the three coverage criteria.
References
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Journal ArticleDOI
Search‐based software test data generation: a survey
TL;DR: Some of the work undertaken in the use of metaheuristic search techniques for the automatic generation of test data is surveyed, discussing possible new future directions of research for each of its different individual areas.
Journal ArticleDOI
The N-Version Approach to Fault-Tolerant Software
TL;DR: Principal requirements for the implementation of N-version software are summarized and the DEDIX distributed supervisor and testbed for the execution of N -version software is described.
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