Project Teamwork Assessment and Success Rate Prediction Through Meta-Heuristic Algorithms

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Author(s): Soumen Mukherjee (RCC Institute of Information Technology, India), Arup Kumar Bhattacharjee (RCC Institute of Information Technology, India)and Arpan Deyasi (RCC Institute of Information Technology, India)
Copyright: 2019
Pages: 29
Source title: Interdisciplinary Approaches to Information Systems and Software Engineering
Source Author(s)/Editor(s): Alok Bhushan Mukherjee (North-Eastern Hill University Shillong, India)and Akhouri Pramod Krishna (Birla Institute of Technology Mesra, India)
DOI: 10.4018/978-1-5225-7784-3.ch003

Keywords: Computer Science & IT / Information Science Reference / Systems & Software Design / Systems and Software Engineering

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Abstract

In this chapter, machine learning algorithms along with association rule analysis are applied to measure how the project teamwork success rate depends on various technical and soft skill factors of a software project. A real-life dataset is taken form UCI archive on project teamwork, which comprises of 84 features or attributes with 64 samples. The most effective feature set is therefore selected using meta-heuristic algorithms (i.e., particle swarm optimization [PSO] and simulated annealing [SA]) and then the data are given to support vector machine (SVM) and k-nearest neighbor (KNN) classifier for classification. Association rule mining is also used for rule generation among the different features of software project team to determine support and confidence. This chapter deals with how the project-based learning helps to manifest the students towards professionalism.

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