In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE life cycle, from requirements and project planning to maintenance and reengineering. 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 paper1 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.

e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission Writing software is a difficult and expensive task. Its automation is hence very valuable. Search algorithms have been successfully used to tackle many software engineering problems. Unfortunately, for some problems the traditional techniques have been of only limited scope, and search algorithms have not been used yet. We hence propose a novel framework that is based on a co-evolution of programs and test cases to tackle these difficult problems. This framework can be used to tackle software engineering tasks such as Automatic Refinement, Fault Correction and Improving Non-functional Criteria. These tasks are very difficult, and their automation in literature has been limited. To get a better understanding of how search algorithms work, there is the need of a theoretical foundation. That would help to get better insight of search based software engineering. We provide first theoretical analyses for search based software testing, which is one of the main components of our co-evolutionary framework. This thesis gives the important contribution of presenting a novel framework, and we then study its application to three difficult software engineering problems. In this thesis we also give the important contribution of defining a first theoretical foundation. Acknowledgements

Abstract—Software testing is an important issue in software engineering. As software systems become increasingly large and complex, the problem of how to optimally allocate the limited testing resource during the testing phase has become more important, and difficult. Traditional Optimal Testing Resource Allocation Problems (OTRAPs) involve seeking an optimal allocation of a limited amount of testing resource to a number of activities with respect to some objectives (e.g., reliability, or cost). We suggest solving OTRAPs with Multi-Objective Evolutionary Algorithms (MOEAs). Specifically, we formulate OTRAPs as two types of multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. Second, the total testing resource consumed is also taken into account as the third objective. The advantages of MOEAs over state-of-the-art single objective approaches to OTRAPs will be shown through empirical studies. Our study has revealed that a well-known MOEA, namely Nondominated Sorting Genetic Algorithm II (NSGA-II), performs well on the first problem formulation, but fails on the second one. Hence, a Harmonic Distance Based Multi-Objective Evolutionary Algorithm (HaD-MOEA) is proposed and evaluated in this paper. Comprehensive experimental studies on both parallel-series, and star-structure modular software systems have shown the superiority of HaD-MOEA over NSGA-II for OTRAPs. Index Terms—Multi-objective evolutionary algorithm, parallelseries

Abstract---Software Testing is the process of implementing a program with the definite intent of finding errors former to delivery to the end user. Due to the increase and the complexity of the software system the problem is how to optimally assign the narrow testing resource during the testing phase has become more important and difficult. Traditional Optimal Testing Resource Allocation Problems (OTRAPs) includesin afinest allocation of a limited amount of testing resources with respect to reliability, cost etc. To solve the OTRAPs with Multi-Objective Algorithms called as Hierarchy Particle Swarm Optimization Algorithm (HPSO) is suggested. Especially, organize OTRAPs for two types of multi-objective problems. First one is reliability of the system and the testing cost of the system as two objectives. Second, the total testing resource consumed is also taken into account as the third objective, sensitivity is the fourth objective. The existing algorithms require more time and the both the evolutionary and the NSGA algorithm having more drawbacks. To overcome the drawbacks of the existing algorithm, the proposed HPSO algorithm which is used in this paper, satisfy allthe four objective of this research. Experimental results show that the proposed algorithm is more efficient than the existing algorithms. Keywords---Particle Swarm Optimization algorithm,