Tuning COCOMO-II for Software Process Improvement: A Tool Based Approach

Syeda Umema Hani; Abu Turab Alam; Abdul Basit Shaikh

doi:10.22581/muet1982.1604.03

Syeda Umema Hani Graduate School of Engineering Sciences and Information Technology, Hamdard University, Karachi.
Abu Turab Alam Department of Computer Science, Institute of Business Management, Karachi.
Abdul Basit Shaikh Graduate School of Engineering Sciences and Information Technology, Hamdard University, Karachi.

DOI: https://doi.org/10.22581/muet1982.1604.03

Abstract

In order to compete in the international software development market the software organizations have to adopt internationally accepted software practices i.e. standard like ISO (International Standard Organization) or CMMI (Capability Maturity Model Integration) in spite of having scarce resources and tools. The aim of this study is to develop a tool which could be used to present an actual picture of Software Process Improvement benefits in front of the software development companies. However, there are few tools available to assist in making predictions, they are too expensive and could not cover dataset that reflect the cultural behavior of organizations for software development in developing countries. In extension to our previously done research reported elsewhere for Pakistani software development organizations which has quantified benefits of SDPI (Software Development Process Improvement), this research has used sixty-two datasets from three different software development organizations against the set of metrics used in COCOMO-II (Constructive Cost Model 2000). It derived a verifiable equation for calculating ISF (Ideal Scale Factor) and tuned the COCOMO-II model to bring prediction capability for SDPI (benefit measurement classes) such as ESCP (Effort, Schedule, Cost, and Productivity). This research has contributed towards software industry by giving a reliable and low-cost mechanism for generating prediction models with high prediction accuracy. Hopefully, this study will help software organizations to use this tool not only to predict ESCP but also to predict an exact impact of SDPI.