Using artificial bee colony to optimize software quality estimation models. (c2015)

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dc.contributor.author Abou Assi, Tatiana Antoine
dc.date.accessioned 2016-04-06T05:20:33Z
dc.date.available 2016-04-06T05:20:33Z
dc.date.copyright 9/14/2015 en_US
dc.date.issued 2016-04-06
dc.identifier.uri http://hdl.handle.net/10725/3492
dc.description.abstract Computer software has become an important foundation in several versatile domains including medicine, engineering, etc. Consequently, with such widespread application of software, the essential need of ensuring certain software quality characteristics such as efficiency, reliability and stability has emerged. In order to measure such software quality characteristics, we must wait until the software is implemented, tested and put to use for a certain amount of time. Several software metrics have been proposed in the literature to avoid this long and costly process, and they proved to be a good means of estimating software quality. For this purpose, software quality prediction models are built. These are used to establish a relationship between internal sub-characteristics such as inheritance, coupling, size, etc. and external software quality attributes such as maintainability, stability, etc. Using such relationships, one can build a model in order to estimate the quality of new software systems. Such models are mainly constructed by either statistical techniques such as regression, or machine learning techniques such as C4.5 and neural networks. We build our model using machine learning techniques in particular rule-based models. These have a white-box nature which gives the classification as well as the reason for it making them attractive to experts in the domain. In this thesis, we propose a novel heuristic based on Artificial Bee Colony (ABC) to optimize rule-based software quality prediction models. We validate our technique on data describing maintainability and reliability of classes in an Object-Oriented system. We compare our models to others constructed using other well established techniques such as C4.5, Genetic Algorithms, Simulated Annealing, Tabu Search, multi-layer perceptron with back-propagation, multi-layer perceptron hybridized with ABC and the majority classifier. Results show that, in most cases, our proposed technique out-performs the others in different aspects. en_US
dc.language.iso en en_US
dc.subject Computer software -- Quality control en_US
dc.subject Software measurement en_US
dc.subject Swarm intelligence en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title Using artificial bee colony to optimize software quality estimation models. (c2015) en_US
dc.type Thesis en_US
dc.title.subtitle a case of maintainability and reliability en_US
dc.author.degree MS in Computer Science en_US
dc.author.school SAS en_US
dc.author.idnumber 201206297 en_US
dc.author.commembers Harmanani, Haidar
dc.author.commembers Khazen, George
dc.author.commembers Takchi, Jean
dc.author.woa OA en_US
dc.author.department Computer Science and Mathematics en_US
dc.description.embargo N/A en_US
dc.description.physdesc 1 hard copy: xix, 155 leaves; ill. (some col.); 30 cm. available at RNL. en_US
dc.author.advisor Azar, Danielle
dc.keywords Software Quality en_US
dc.keywords Software Quality Metrics en_US
dc.keywords Maintainability en_US
dc.keywords Stability en_US
dc.keywords Reliability en_US
dc.keywords Software Defect en_US
dc.keywords Predictive Models en_US
dc.keywords Artificial Bee Colony (ABC) en_US
dc.keywords Swarm Intelligence en_US
dc.keywords Heuristics en_US
dc.keywords Optimization en_US
dc.keywords Search-Based Software Engineering (SBSE) en_US
dc.keywords Machine Learning en_US
dc.keywords C4.5 en_US
dc.description.bibliographiccitations Bibliography: leaves 129-147. en_US
dc.identifier.doi https://doi.org/10.26756/th.2015.48 en_US
dc.publisher.institution Lebanese American University en_US

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