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Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation

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dc.citedby1
dc.contributor.authorMat Jani H.en_US
dc.contributor.authorLee S.P.en_US
dc.contributor.authorid13609136000en_US
dc.contributor.authorid55664303000en_US
dc.date.accessioned2023-12-29T07:55:21Z
dc.date.available2023-12-29T07:55:21Z
dc.date.issued2009
dc.description.abstractObject-oriented application framework is one of the most important implementations of object-oriented software engineering. Normally, a user takes several months of learning in order to become highly productive in using a specific object-oriented application framework. Without proper documentation, frameworks are not very usable to framework users. Currently available framework documentation approaches are not very effective for new framework users, and this scenario tends to discourage new users in using frameworks. The main objective of this paper is to propose and implement an intelligent framework documentation approach that integrates case-based learning (CBL) with genetic algorithm (GA) and Knuth-Morris-Pratt (KMP) pattern matching algorithm with the intention of making learning a framework more effective. GA assists in optimizing the search process and performs machine learning. Within the GA, nearest neighbor algorithm is used in determining the most similar recorded case that can be used in solving the new case. A new case is retained in the case base for future retrievals. A framework user is allowed to select from a list of features provided by the framework that he or she is interested in learning, and the system will give an example of application related to the selected features. This paper concludes with a prototype that implements the intelligent framework documentation approach. � 2009 Springer Berlin Heidelberg.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-642-01112-2_21
dc.identifier.epage213
dc.identifier.scopus2-s2.0-65449152946
dc.identifier.spage202
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-65449152946&doi=10.1007%2f978-3-642-01112-2_21&partnerID=40&md5=f38fceb4cc60259c194a70abc2951e83
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30891
dc.identifier.volume20 LNBIP
dc.pagecount11
dc.publisherSpringer Verlagen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Business Information Processing
dc.subjectFramework documentation
dc.subjectGenetic algorithm (GA)
dc.subjectKnuth-Morris-Pratt (KMP) pattern matching algorithm
dc.subjectApplications
dc.subjectEducation
dc.subjectGenetic algorithms
dc.subjectInformation systems
dc.subjectLearning systems
dc.subjectObject oriented programming
dc.subjectPattern matching
dc.subjectSoftware engineering
dc.subjectApplication frameworks
dc.subjectCase base
dc.subjectCase-based learning
dc.subjectFramework documentation
dc.subjectKnuth-Morris-Pratt (KMP) pattern matching algorithm
dc.subjectMachine-learning
dc.subjectNearest neighbor algorithms
dc.subjectObject-oriented
dc.subjectObject-oriented software engineerings
dc.subjectSearch process
dc.subjectLearning algorithms
dc.titleUsing GA and KMP algorithm to implement an approach to learning through intelligent framework documentationen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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