Offshore Software Maintenance Outsourcing Predicting Clients Proposal using Supervised Learning

TL;DR

This paper explores using supervised machine learning classifiers to predict the most suitable offshore software maintenance outsourcing client proposal, aiming to assist vendors in decision-making.

Contribution

It introduces a supervised learning approach for predicting OSMO client proposals, demonstrating high accuracy with classifiers like Naive Bayesian, SMO, and Logistic regression.

Findings

Logistic classifier achieved 87.27% accuracy.

Supervised learning outperforms other methods for proposal prediction.

Survey data from OSMO vendors supports model effectiveness.

Abstract

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 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 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 Na\"ive Bayesian, SMO, Logistics apprehended 69.75, 81.81, and 87.27 percent testing accuracy respectively. This study concludes that supervised learning is the most suitable technique to predict the OSMO client's proposal.