In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to software engineering in which search based optimisation algorithms are used to address problems in Software Engineering. SBSE has been applied to problems throughout the Software Engineering lifecycle, from requirements and project planning to maintenance and re-engineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This paper 1 provides a review and classification of literature on SBSE. The paper identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

In this paper we analyze the application of parallel and sequential evolutionary algorithms to the automatic test data generation problem. The problem consists of automatically creating a set of input data to test a program. This is a fundamental step in software development and a time consuming task in existing software companies. Canonical sequential evolutionary algorithms have been used in the past for this task. We explore here the use of parallel evolutionary algorithms. Evidence of greater efficiency, larger diversity maintenance, additional availability of memory/CPU, and multi-solution capabilities of the parallel approach, reinforce the importance of the advances in research with these algorithms. We describe in this work how canonical genetic algorithms (GAs) and evolutionary strategies (ESs) can help in software testing, and what the advantages are (if any) of using decentralized populations in these techniques. In addition, we study the influence of some parameters of the proposed test data generator in the results. For the experiments we use a large benchmark composed of twelve programs that includes fundamental algorithms in computer science.

Software specification is an important element of the software development process. However, in most cases the specifications are out-of-date or even missing. One solution for this kind of problem is to use some process that infers the specification automatically. Work by Ernst et al [9, 22] has shown how specifications can be generated using program execution traces. These approaches are dependent on the test suites used to produce the traces, which may lead to unreliable specifications being inferred. Such specification inference is highly useful, however. In this paper we show how search based testing techniques can challenge and identify erroneous elements of such inferred specifications. This leads to a much tighter (accurate) inferred specifications. Thus, specification inference and search based test data generation are shown to be complementary. 1

Abstract. This paper 1 provides a motivation for the application of search based optimization to Software Engineering, an area that has come to be known as Search Based Software Engineering (SBSE). SBSE techniques have already been applied to many problems throughout the Software Engineering lifecycle, with new application domains emerging on a regular basis. The approach is very generic and therefore finds wide application in Software Engineering. It facilitates automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. Previous work has already discussed, in some detail, the advantages of the SBSE approach for Software Engineering. This paper summarises previous work and goes further, by arguing that Software Engineering provides the ideal set of application problems for which optimization algorithms are supremely well suited.

Search based software testing has emerged in recent years as an important research area within automated software test data generation. The general approach of couching the satisfaction of test goals as numerical optimisation problems has been applied to a variety of problems such as satisfying structural coverage criteria, specification falsification, exception generation, breaking unit pre-conditions and software hazard discovery. However, some test goals may be hard to satisfy. For example, a program branch may be difficult to reach via a search based technique, because the domain of the data that causes it to be taken is exceedingly small or the non-linearity of the “fitness landscape ” precludes the provision of effective guidance to the search for test data. In this paper we propose to “stretch ” relevant conditions within a program to make them easier to satisfy. We find test data that satisfies the corresponding test goal of the stretched program. We then seek to transform the stretched program by stages back to the original, simultaneously migrating the obtained test data to produce test data that satisfies the goal for the original program. The “stretching” device is remarkably simple and shows significant promise for obtaining hard-to-find test data and also gives efficiency improvements over standard search based testing approaches.

Abstract—The Future Internet will be a complex interconnection of services, applications, content and media, on which our society will become increasingly dependent. Time to market is crucial in Internet applications and hence release cycles grow ever shorter. This, coupled with the highly dynamic nature of the Future Internet will place new demands on software testing. Search–Based Testing is ideally placed to address these emerging challenges. Its techniques are highly flexible and robust to only partially observable systems. This paper presents an overview of Search–Based Testing and discusses some of the open challenges remaining to make search–based techniques applicable to the Future Internet. Index Terms—evolutionary testing; search–based testing; research topics. I.