Numerous research studies have claimed that search-basedalgorithms have the potential to be effectively used in various software engineering domains. An important task in software organizations is to efficiently reco...
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Numerous research studies have claimed that search-basedalgorithms have the potential to be effectively used in various software engineering domains. An important task in software organizations is to efficiently recognize change prone classes of a software, as it is crucial to plan efficient resource utilization and to take precautionary design measures as early as possible in the software product lifecycle. This assures development of good quality software products at lower costs. The current study attempts to evaluate the capability of search-basedalgorithms while developing prediction models for identification of the change prone classes in a software. Though previous literature has evaluated the use of statistical category and machine learning category of algorithms in this domain, the suitability of search-basedalgorithms needs extensive investigation in this area. Furthermore, the study compares the performance of search-based classifiers with statistical and machine learning classifiers, by empirically validating the results on fourteen open source data sets. The results indicate comparable and in some cases even better performance of search based algorithms in comparison to other evaluated categories of algorithms.
There are numerous reasons leading to change in software such as changing requirements, changing technology, increasing customer demands, fixing of defects etc. Thus, identifying and analyzing the change-prone classes...
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There are numerous reasons leading to change in software such as changing requirements, changing technology, increasing customer demands, fixing of defects etc. Thus, identifying and analyzing the change-prone classes of the software during software evolution is gaining wide importance in the field of software engineering. This would help software developers to judiciously allocate the resources used for testing and maintenance. Software metrics can be used for constructing various classification models which can be used for timely identification of change prone classes. search based algorithms which form a subset of machine learning algorithms can be utilized for constructing prediction models to identify change prone classes of software. search based algorithms use a fitness function to find the best optimal solution among all the possible solutions. In this work, we analyze the effectiveness of hybridized search based algorithms for change prediction. In other words, the aim of this work is to find whether search based algorithms are capable for accurate model construction to predict change prone classes. We have also constructed models using machine learning techniques and compared the performance of these models with the models constructed using search based algorithms. The validation is carried out on two open source Apache projects, Rave and Commons Math. The results prove the effectiveness of hybridized search based algorithms in predicting change prone classes of software. Thus, they can be utilized by the software developers to produce an efficient and better developed software. (C) 2016 Elsevier Ltd. All rights reserved.
Owing to an exponential increase in computational time associated with increasing number of components, exhaustive testing is impractical. Here, many researchers opt to adopt pairwise testing to minimize the overall n...
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ISBN:
(纸本)9781467367226
Owing to an exponential increase in computational time associated with increasing number of components, exhaustive testing is impractical. Here, many researchers opt to adopt pairwise testing to minimize the overall number of tests. Recently, many existing work are focusing on the use of search-basedalgorithms as the basis of the implementation algorithm. This paper presents a critical comparison of search-based algorithm for generating the pairwise test suite. An analysis of existing SB pairwise strategies shows the positive and negative points for each strategy thereby highlighting promising future directions in this area.
Nautilus Framework allows practitioners to develop and experiment with several multi- and many-objective evolutionary algorithms-guided (or not) by human participation-in a few steps with a minimum required background...
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Nautilus Framework allows practitioners to develop and experiment with several multi- and many-objective evolutionary algorithms-guided (or not) by human participation-in a few steps with a minimum required background in coding and search-basedalgorithms.
searchbased Software Engineering (SBSE) is the field of software engineering research and practice that applies searchbased techniques to solve different optimization problems from diverse software engineering areas...
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searchbased Software Engineering (SBSE) is the field of software engineering research and practice that applies searchbased techniques to solve different optimization problems from diverse software engineering areas. SBSE approaches allow software engineers to automatically obtain solutions for complex and labor-intensive tasks, contributing to reduce efforts and costs associated to the software development. The SBSE field is growing rapidly in Brazil. The number of published works and research groups has significantly increased in the last three years and a Brazilian SBSE community is emerging. This is mainly due to the Brazilian Workshop on searchbased Software Engineering (WOES), co-located with the Brazilian Symposium on Software Engineering (SBES). Considering these facts, this paper presents results of a mapping we have performed in order to provide an overview of the SBSE field in Brazil. The main goal is to map the Brazilian SBSE community on SBES by identifying the main researchers, focus of the published works, fora and frequency of publications. The paper also introduces SBSE concerns and discusses trends, challenges, and open research problems to this emergent area. We hope the work serves as a reference to this novel field, contributing to disseminate SBSE and to its consolidation in Brazil. (c) 2012 Elsevier Inc. All rights reserved.
Cloud computing promises scalable hosting by offering an elastic management of virtual machines which run on top of hardware data centers. This elastic management as a cornerstone of PaaS (Platform As A Service) has t...
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ISBN:
(纸本)9781450324694
Cloud computing promises scalable hosting by offering an elastic management of virtual machines which run on top of hardware data centers. This elastic management as a cornerstone of PaaS (Platform As A Service) has to deal with trade-offs between conflicting requirements such as cost and quality of service. Solving such trade-offs is a challenging problem. Indeed, most of PaaS providers consider only one optimization axis or ad-hoc multi-objective resolution techniques using domain specific *** paper aims at proposing a generic approach to build cloud optimization by combining modeling and searchbased paradigms. Our approach is two-fold: 1) To reason about a cloud environment, we use a [email protected] approach to have an abstraction layer of a cloud configuration that supports monitoring capabilities and represents cloud intrinsic parameters like cost, load information, etc. 2) We use a search-based algorithm to navigate through cloud candidate configuration solutions in order to solve the Cloud Multi-objective Optimization Problem (CMOP).We validate our approach based on a case study that we define with our cloud provider partner EBRC as representative of a dynamic management problem of heterogeneous distributed cloud nodes. We implement a prototype of our PaaS supervision framework using Kevoree, a [email protected] platform. The prototype shows the efficiency of our approach in terms of finding possible cloud configurations in reasonable time. The prototype is flexible since it enables an easy reconfiguration of the cloud customer optimization objectives.
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