Mehran University Research Journal Of Engineering &
Technology (HEC Recognized In Category "X")
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Tuning COCOMO-II for Software Process Improvement: A Tool Based Approach

Keywords: Software Process Improvement, SPI Benefits, Capability Maturity Model Integration, COCOMO, Process Performance, Process Performance Models PPM, Metrics

Mehran University Research Journal of Engineering & Technology

Volume 35 ,  Issue 4

SYEDA UMEMA   HANI,ABU TURAB ALAM  ,ABDUL BASIT SHAIKH  

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.