Abstract-Regression testing is an expensive, but important, process. Unfortunately, there may be insufficient resources to allow for the reexecution of all test cases during regression testing. In this situation, test case prioritization techniques aim to improve the effectiveness of regression testing by ordering the test cases so that the most beneficial are executed first. Previous work on regression test case prioritization has focused on Greedy Algorithms. However, it is known that these algorithms may produce suboptimal results because they may construct results that denote only local minima within the search space. By contrast, metaheuristic and evolutionary search algorithms aim to avoid such problems. This paper presents results from an empirical study of the application of several greedy, metaheuristic, and evolutionary search algorithms to six programs, ranging from 374 to 11,148 lines of code for three choices of fitness metric. The paper addresses the problems of choice of fitness metric, characterization of landscape modality, and determination of the most suitable search technique to apply. The empirical results replicate previous results concerning Greedy Algorithms. They shed light on the nature of the regression testing search space, indicating that it is multimodal. The results also show that Genetic Algorithms perform well, although Greedy approaches are surprisingly effective, given the multimodal nature of the landscape.

Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing.

Metaheuristic techniques such as genetic algorithms, simulated annealing and tabu search have found wide application in most areas of engineering. These techniques have also been applied in business, financial and economic modelling. Metaheuristics have been applied to three areas of software engineering: test data generation, module clustering and cost/effort prediction, yet there remain many software engineering problems which have yet to be tackled using metaheuristics. It is surprising that metaheuristics have not been more widely applied to software engineering; many problems in software engineering are characterised by precisely the features which make metaheuristics search applicable. In the paper it is argued that the features which make metaheuristics applicable for engineering and business applications outside software engineering also suggest that there is great potential for the exploitation of metaheuristics within software engineering. The paper briefly reviews the principal metaheuristic search techniques and surveys existing work on the application of metaheuristics to the three software engineering areas of test data generation, module clustering and cost/effort prediction. It also shows how metaheuristic search techniques can be applied to three additional areas of software engineering: maintenance/evolution system integration and requirements scheduling. The software engineering problem areas considered thus span the range of the software development process, from initial planning, cost estimation and requirements analysis through to integration, maintenance and evolution of legacy systems. The aim is to justify the claim that many problems in software engineering can be reformulated as search problems, to which metaheuristic techniques can be applied. The goal of the paper is to stimulate greater interest in metaheuristic search as a tool of optimisation of software engineering problems and to encourage the investigation and exploitation of these technologies in finding near optimal solutions to the complex constraint-based scenarios which arise so frequently in software engineering

A testability transformation is a source-to-source transformation that aims to improve the ability of a given test generation method to generate test data for the original program. We introduce testability transformation, demonstrating that it differs from traditional transformation, both theoretically and practically, while still allowing many traditional transformation rules to be applied. We illustrate the theory of testability transformation with an example application to evolutionary testing. An algorithm for flag removal is defined and results are presented from an empirical study which show how the algorithm improves both the performance of evolutionary test data generation and the adequacy level of the test data so-generated

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--Here the method proposed in [13] for constructing minimal-length checking sequences based on distinguishing sequences is improved. The improvement is based on optimizations of the state recognition sequences and their use in constructing test segments. It is shown that the proposed improvement further reduces the length of checking sequences produced from minimal, completely specified, and deterministic finite state machines.

Abstract

There is much research that shows people's mood can affect their activities. This paper argues that this also applies to programmers, especially their debugging. Literature-based framework is presented linking programming with various cognitive activities as well as linking cognitive activities with moods. Further, the effect of mood on debugging was tested in two experiments. In the first experiment, programmers (n = 72) saw short movie clips selected for their ability to provoke specific moods. Afterward, they completed a debugging test. Results showed the video clips had a significant effect on programmers' debugging performance; especially, there was a significant difference after watching low-and high-arousalevoking video clips. In the second experiment, programmers' mood was manipulated by asking participants (n = 19) to dry run algorithms for at least 16 min. They performed some physical exercises before continuing dry running algorithms again. The results showed a significant increase in arousal and valence that coincided with an improvement in programmers' task performance after the physical exercises. Together, this suggests that programmers' moods influence some programming tasks such as debugging.