An Ant Colony Optimization Heuristic to Optimize Prediction of Stability of Object-Oriented Components
The IEEE 729-1983 Standard defines software quality as "the composite characteristics of software that determine the degree to which the software in use will meet the expectations of the customer." Assessing software quality in the early stages of design and development is crucial in reduc...
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| Format: | conferenceObject |
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2017
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| Online Access: | http://hdl.handle.net/10725/5372 http://dx.doi.org/10.1109/IRI.2015.45 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php http://ieeexplore.ieee.org/abstract/document/7300981/ |
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| Summary: | The IEEE 729-1983 Standard defines software quality as "the composite characteristics of software that determine the degree to which the software in use will meet the expectations of the customer." Assessing software quality in the early stages of design and development is crucial in reducing time and effort. Various metrics have been proposed for estimating software quality characteristics from measurable attributes. This paper presents an Ant Colony Optimization (ACO) approach that improves the prediction accuracy of software quality estimation models by intensifying the search around the metric neighborhood. The method has been implemented, and favorable results comparisons are reported. |
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