Search Based Software Engineering

The articles in this special section focus on search-based software engineering. Search Based Software Engineering (SBSE) consists of the application of computational intelligence (CI) algorithms to hard optimization problems in software engineering (SE). It has become an important application field for CI. The term SBSE was coined by Harman and Jones in 2001, although there was work on the application of CI algorithms to SE before this date. After more than fifteen years development, CI algorithms have been used to solve SE tasks in almost all the stages of an SE lifecycle, including requirements, designing, coding, testing and maintenance. solved by three steps

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.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 lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work 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.EPSRC and E

Cloud engineering is search based software engineering too

Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE

Search Based Software Engineering

Abstract This paper reviews the search based software engineering research and finds the major milestones in this direction. The SBSE approach has been the topic of several surveys and reviews. Search Based Software Engineering (SBSE) consists of the application of search-based optimization to software engineering. Using SBSE, a software engineering task is formulated as a search problem by defining a suitable candidate solution representation and a fitness function to differentiate between solution candidates. This paper gives an overview of major research studies undertaken in the domain

Handling High-Level Model Changes Using Search Based Software Engineering

Model-Driven Engineering (MDE) considers models as first-class artifacts during the software lifecycle. The number of available tools, techniques, and approaches for MDE is increasing as its use gains traction in driving quality, and controlling cost in evolution of large software systems. Software models, defined as code abstractions, are iteratively refined, restructured, and evolved. This is due to many reasons such as fixing defects in design, reflecting changes in requirements, and modifying a design to enhance existing features. In this work, we focus on four main problems related to the evolution of software models: 1) the detection of applied model changes, 2) merging parallel evolved models, 3) detection of design defects in merged model, and 4) the recommendation of new changes to fix defects in software models. Regarding the first contribution, a-posteriori multi-objective change detection approach has been proposed for evolved models. The changes are expressed in terms of atomic and composite refactoring operations. The majority of existing approaches detects atomic changes but do not adequately address composite changes which mask atomic operations in intermediate models. For the second contribution, several approaches exist to construct a merged model by incorporating all non-conflicting operations of evolved models. Conflicts arise when the application of one operation disables the applicability of another one. The essence of the problem is to identify and prioritize conflicting operations based on importance and context – a gap in existing approaches. This work proposes a multi-objective formulation of model merging that aims to maximize the number of successfully applied merged operations. For the third and fourth contributions, the majority of existing works focuses on refactoring at source code level, and does not exploit the benefits of software design optimization at model level. However, refactoring at model level is inherently more challenging due to difficulty in assessing the potential impact on structural and behavioral features of the software system. This requires analysis of class and activity diagrams to appraise the overall system quality, feasibility, and inter-diagram consistency. This work focuses on designing, implementing, and evaluating a multi-objective refactoring framework for detection and fixing of design defects in software models.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136077/1/Usman Mansoor Final.pdfDescription of Usman Mansoor Final.pdf : Dissertatio

Model refactoring by example: A multi‐objective search based software engineering approach

Declarative rules are frequently used in model refactoring in order to detect refactoring opportunities and to apply the appropriate ones. However, a large number of rules is required to obtain a complete specification of refactoring opportunities. Companies usually have accumulated examples of refactorings from past maintenance experiences. Based on these observations, we consider the model refactoring problem as a multi objective problem by suggesting refactoring sequences that aim to maximize both structural and textual similarity between a given model (the model to be refactored) and a set of poorly designed models in the base of examples (models that have undergone some refactorings) and minimize the structural similarity between a given model and a set of well‐designed models in the base of examples (models that do not need any refactoring). To this end, we use the Non‐dominated Sorting Genetic Algorithm (NSGA‐II) to find a set of representative Pareto optimal solutions that present the best trade‐off between structural and textual similarities of models. The validation results, based on 8 real world models taken from open‐source projects, confirm the effectiveness of our approach, yielding refactoring recommendations with an average correctness of over 80%. In addition, our approach outperforms 5 of the state‐of‐the‐art refactoring approaches.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143783/1/smr1916.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143783/2/smr1916_am.pd