In software engineering, software maintenance is the process of correction, updating, and improvement of software products after handed over to the customer. Through offshore software maintenance outsourcing (OSMO) clients can get advantages like reduce cost, save time, and improve quality. In most cases, the OSMO vendor generates considerable revenue. However, the selection of an appropriate proposal among multiple clients is one of the critical problems for OSMO vendors. The purpose of this paper is to suggest an effective machine learning technique that can be used by OSMO vendors to assess or predict the OSMO client’s proposal. The dataset is generated through a survey of OSMO vendors working in a developing country. The results showed that supervised learning-based classifiers like Naïve Bayesian, SMO, Logistics apprehended 69.75 %, 81.81 %, and 87.27 % testing accuracy respectively. This study concludes that supervised learning is the most suitable technique to predict the OSMO client's proposal.
A System Dynamics Model for Evaluating the Firms’ Capabilities in Maintenance Outsourcing and Analyzing the Profitability of Outsourcing