Title :
A framework of classifying maintenance requests based on learning techniques
Author :
Mahmoodian, Naghmeh ; Abdullah, Rusli ; Murad, Masrah Azrifah Azmi
Author_Institution :
Fac. of Comput. Sci. & Inf. Technol., Univ. Putra Malaysia, Kuala Lumpur, Malaysia
Abstract :
Classify maintenance request is one of the processes in the large software system to support maintainers in doing their daily maintenance tasks more effectively. Categorizing these maintenance requests are an essential requirement in managing the maintenance request for software maintainer and need a great effort as well as determining classification. Hence, this paper presents the framework from the use of three different classification approaches, namely Bayesian model Decision Tree and Logistic regression. We show that nai¿ve Bayesian classifier, Decision Tree and Logistic regression can be used to accurately classify issues into maintenance type.
Keywords :
belief networks; decision trees; learning (artificial intelligence); pattern classification; regression analysis; software maintenance; Bayesian model; decision tree; learning techniques; logistic regression; maintenance request classification; software maintenance; Bayesian methods; Classification tree analysis; Computer science; Decision trees; Information technology; Logistics; Regression tree analysis; Software maintenance; Software performance; Software systems; classification; software maintenance; type of software maintenance;
Conference_Titel :
Information Retrieval & Knowledge Management, (CAMP), 2010 International Conference on
Conference_Location :
Shah Alam, Selangor
Print_ISBN :
978-1-4244-5650-5
DOI :
10.1109/INFRKM.2010.5466908