A hybrid heuristic approach to optimize rule based software quality estimation models. (c2008)

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dc.contributor.author Korkmaz, Rita
dc.date.accessioned 2011-10-06T10:31:27Z
dc.date.available 2011-10-06T10:31:27Z
dc.date.copyright 2008 en_US
dc.date.issued 2011-10-06
dc.date.submitted 2008-06-04
dc.identifier.uri http://hdl.handle.net/10725/697
dc.description Includes bibliographical references (l. 96-100). en_US
dc.description.abstract Software quality is defined as the degree to which a software component or system meets specified requirements and specifications. Objectively quantifying software quality is important as it reduces cost, effort and time. Nonetheless, assessment of software quality is a difficult task since most software quality characteristics such as maintainability, reliability and reusability cannot be directly measured. However, they can be predicted from other software quality attributes such as complexity and inheritance. Many metrics to measure these software quality attributes have been proposed. These metrics are used to build software quality estimation models that take different forms: statistical models, rule based models and decision trees. In our work, we will deal with rule based estimation models for two main reasons: First, their white box nature makes them easy to interpret by human experts. Second, they supply guidelines that clearly show how to reach the prediction. Since data used to build such estimation models is scarce in the software quality estimation field, the accuracy of these rule based models gravely deteriorates when applied on new unseen data. The goal of this thesis is to explore and assess the use of hybrid heuristics to improve and adapt the rule based models to the context specific datasets. en_US
dc.language.iso en en_US
dc.subject Computer software -- Quality control en_US
dc.subject Genetic algorithms en_US
dc.subject Algorithms en_US
dc.subject Simulated annealing (Mathematics) en_US
dc.title A hybrid heuristic approach to optimize rule based software quality estimation models. (c2008) en_US
dc.type Thesis en_US
dc.term.submitted Spring en_US
dc.author.degree MS in Computer Science en_US
dc.author.school Arts and Sciences en_US
dc.author.idnumber 200301473 en_US
dc.author.commembers Dr. Haidar Harmanani
dc.author.commembers Dr. Chadi Nour
dc.author.woa OA en_US
dc.description.physdesc 1 bound copy: 100 leaves; ill. (some col.); 31 cm. Available at RNL. en_US
dc.author.division Computer Science en_US
dc.author.advisor Dr. Danielle Azar
dc.identifier.doi https://doi.org/10.26756/th.2008.19 en_US
dc.publisher.institution Lebanese American University en_US

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