Author :
Lucca, G. A Di ; Penta, M. Di ; Gradara, S.
Abstract :
When a software system critical for an organization exhibits a problem during its operation, it is relevant to fix it in a short period of time, to avoid serious economical losses. The problem is therefore noticed by the organization in charge of the maintenance, and it should be correctly and quickly dispatched to the right maintenance team. We propose to automatically classify incoming maintenance requests (also said tickets), routing them to specialized maintenance teams. The final goal is to develop a router working around the clock, that, without human intervention, dispatches incoming tickets with the lowest misclassification error, measured with respect to a given routing policy. 6000 maintenance tickets from a large, multi-site, software system, spanning about two years of system in-field operation, were used to compare and assess the accuracy of different classification approaches (i.e., Vector Space model, Bayesian model, support vectors, classification trees and k-nearest neighbor classification). The application and the tickets were divided into eight areas and pre-classified by human experts. Preliminary results were encouraging, up to 84% of the incoming tickets were correctly classified.
Keywords :
Bayes methods; classification; learning (artificial intelligence); learning automata; program debugging; software development management; software maintenance; Bayesian model; Support Vector Machine; Vector Space model; classification trees; economical loss; k-nearest neighbor classification; machine learning; maintenance request classification; multi-site software system; organization; software maintenance request classification; Application software; Bayesian methods; Classification tree analysis; Clocks; Extraterrestrial measurements; Humans; Routing; Software maintenance; Software measurement; Software systems;
