Abstract—Effort estimation is crucial for the control, quality and success of any software development project and it become even more crucial in GSD where stakeholders are from different background and interest and there is a huge cultural, linguistic and temporal difference involved between them. Software Effort estimation techniques fall under the categories of Expert judgment, Algorithmic estimation and Machine Learning. In this study a comparative analysis is made between traditional effort estimation techniques and ML techniques. Results show us that ML methods give us more accurate effort estimation as compared to the traditional methods of effort estimation. Moreover the comparisons of different ML techniques are done in this paper to study that which ML method is more suitable in which situation.
Index Terms—Global software development (GSD), artificial intelligence (AI), machine learning (ML)
M. Humayun is with HIT Harbin, China (e-mail: mamoona@hit.edu.cn)
Cui Gang is with HIT, Harbin, China (e-mail: cg@hit.edu.cn)
Cite: Mamoona Humayun and Cui Gang, "Estimating Effort in Global Software Development Projects Using Machine Learning Techniques," International Journal of Information and Education Technology vol. 2, no. 3, pp. 208-211, 2012.