000			03614nam a22004457a 4500
003			CUTN
005			20250703101400.0
008			250703b |||||||| |||| 00| 0 eng d
020			_a9781591409410
041			_aEnglish
082			_222 _a005.1 _bZHA
100			_aZhang, Du.
245			_aAdvances in machine learning applications in software engineering/ _cDu Zhang and Jeffery J.P. Tsai
260			_aUnited States: _bIdea Group Publishing, _c2007.
300			_axv, 480 p. : _c24 cm.
505			_aSection I: Data Analysis and Refinement Chapter I _tA Two-Stage Zone Regression Method for Global Characterization of a Project Database
505			_aChapter II _tIntelligent Analysis of Software Maintenance Data
505			_aChapter III _tImproving Credibility of Machine Learner Models in Software Engineering
505			_aChapter IV _tILP Applications to Software Engineering
505			_aChapter V _tMMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework
505			_aChapter VI _tA Genetic Algorithm-Based QoS Analysis Tool for Reconfigurable Service-Oriented Systems
505			_aSection II: Applications to Software Development Chapter IV _tILP Applications to Software Engineering.
505			_aChapter V _tMMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework
505			_aChapter VI _tA Genetic Algorithm-Based QoS Analysis Tool for Reconfigurable Service-Oriented Systems
505			_aSection III: Predictive Models for Software Quality and Relevancy Chapter VII _tFuzzy Logic Classifiers and Models in Quantitative Software Engineering
505			_aChapter VIII _tModeling Relevance Relations Using Machine Learning Techniques.
505			_aChapter IX _tA Practical Software Quality Classification Model Using Genetic Programming..
505			_aChapter X _tA Statistical Framework for the Prediction of Fault-Proneness..
505			_aChapter XI _tApplying Rule Induction in Software Prediction
505			_aChapter XII _tApplication of Genetic Algorithms in Software Testing
505			_aSection V: Areas of Future Work Chapter XIII _tFormal Methods for Specifying and Analyzing Complex Software Systems.
505			_aChapter XIV _tPractical Considerations in Automatic Code Generation
505			_aChapter XV _tDPSSEE: A Distributed Proactive Semantic Software Engineering Environment
505			_aChapter XVI _tAdding Context into an Access Control Model for Computer Security Policy
520			_aMachine learning is the study of building computer programs that improve their performance through experience. To meet the challenge of developing and maintaining larger and complex software systems in a dynamic and changing environment, machine learning methods have been playing an increasingly important role in many software development and maintenance tasks. Advances in Machine Learning Applications in Software Engineering provides analysis, characterization, and refinement of software engineering data in terms of machine learning methods. This book depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality. Advances in Machine Learning Applications in Software Engineering offers readers direction for future work in this emerging research field.
650			_aMachine Learning
650			_aSoftware Engineering
700			_aTsai, Jeffery J.P.
942			_2ddc _cBOOKS
999			_c44768 _d44768