By Du Zhang, Jeffrey J. P. Tsai
Laptop studying is the research of establishing laptop courses that enhance their functionality via adventure. to satisfy the problem of constructing and keeping better and complicated software program structures in a dynamic and altering atmosphere, computer studying equipment were enjoying an more and more very important position in lots of software program improvement and upkeep initiatives. Advances in laptop studying purposes in software program Engineering presents research, characterization, and refinement of software program engineering facts when it comes to laptop studying equipment. This e-book depicts functions of a number of computing device studying ways in software program structures improvement and deployment, and using computing device studying the right way to determine predictive versions for software program caliber. Advances in computing device studying functions in software program Engineering additionally bargains readers path for destiny paintings during this rising learn box
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Additional info for Advances in Machine Learning Applications in Software Engineering
_Incorrect makes. _Incorrect & LINES OF CODE is in the range <141, 481> & COMPLEXITY is Easy & FUNCTIONALITY is Computational then MULTIPLE COMPONENTS are examined Time Needed to Eliminate a Defect The prediction rates for the models representing effort needed to eliminate a defect are presented in Table 10. In this case, the highest prediction rate is obtained using the model generated by GAGP method. Table 10. 22% Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc.
It is based on the utilization of the following ideas: • Application of a number of rule-based models constructed using different methods on the same/or different subsets of data: This provides means for thorough exploitation of different extraction techniques and increases possibilities of discovering signiﬁcant facets of knowledge embedded in the data. • Application of the concept of basic belief masses from evidence theory (Shafer, 1976; Smets, 1988) that are used to represent goodness of the rules: This provides assessment of quality of the rules from the point of view of their prediction capabilities.
These models proved to provide reasonable accuracy (Schneidewind, 1995, 1997). Many different software metrics are utilized as predictor (input) variables. The most common ones are complexity and size metrics, testing metrics, and process quality data. The most popular software prediction models are models predicting quality-related aspects of software modules. A number of these models have been reported in the literature: • Tree-Based Models: Both classiﬁcation and regression trees are used to categorize software modules and functions; different regression tree algorithms—CART1-LS (least squares), S-PLUS, and CART-LAD (least absolute deviation)—are used to build trees to predict the number of faults in modules in Khoshgoftaar and Seliya (2002); Copyright © 2007, Idea Group Inc.
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