Offshore Software Maintenance Outsourcing Predicting Clients Proposal using Supervised Learning
This work addresses a critical selection problem for OSMO vendors in developing countries, but it is incremental as it applies existing supervised learning methods to a new dataset.
The paper tackled the problem of selecting appropriate client proposals for offshore software maintenance outsourcing vendors by developing a supervised learning model to predict proposals, achieving testing accuracies of up to 87.27% with logistic regression.
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 Naïve 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.