Search Based Software Engineering: Techniques, Taxonomy, Tutorial

Abstract

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...

Achievements, Open Problems and Challenges for Search Based Software Testing

“…We might formulate this problem as a co-evolutionary search that seeks to partition the set of programs of interest, on the one hand, while simultaneously partitioning the set of mutants on the other. Both competitive [6] and cooperative [101] co-evolution have been used in the SBSE literature, but only competitive coevolution has been used in SBST, and co-evolution, in general, remains relatively under-explored in the SBSE literature [56].…”

“…In order for SBSE to be a viable approach, we need a representation, fitness function and a search space that is sufficiently large to make enumeration infeasible [58]. Assignment problems typically come with some form of representation, r that captures the mapping between solution and problem instances.…”

“…SBSE uses computational search techniques to tackle software engineering problems (testing problems in the case of SBST), typified by large complex search spaces [58]. Test objectives find natural counterparts as the fitness functions used by SBSE to guide automated search, thereby facilitating SBSE formulations of many (and diverse) testing problems.…”

“…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].…”

Stopping duplicate bug reports before they start with Continuous Querying for bug reports

“…Continuous Query research can be bolstered with search based software engineering Harman et al (2012), IR search techniques employed by Panichella et al Panichella et al (2016), and query quality and reformulation techniques proposed by Haiduc et al Haiduc (2014).…”