Utility of CK Metrics in Predicting Size of Board-Based Software Games

Nosheen Sabhat; Ali Afzal Malik; Farooque Azam

doi:10.22581/muet1982.1704.22

Nosheen Sabhat Department of Computer Engineering, National University of Sciences & Technology, Islamabad
Ali Afzal Malik Department of Computer Science, National University of Computer and Emerging Sciences, Lahore
Farooque Azam Department of Computer Engineering, National University of Sciences and Technology, Islamabad

DOI: https://doi.org/10.22581/muet1982.1704.22

Abstract

Software size is one of the most important inputs of many software cost and effort estimation models. Early estimation of software plays an important role at the time of project inception. An accurate estimate of software size is, therefore, crucial for planning, managing, and controlling software development projects dealing with the development of software games. However, software size is unavailable during early phase of software development. This research determines the utility of CK (Chidamber and Kemerer) metrics, a well-known suite of object-oriented metrics, in estimating the size of software applications using the information from its UML (Unified Modeling Language) class
diagram. This work focuses on a small subset dealing with board-based software games. Almost sixty games written using an object-oriented programming language are downloaded from open source repositories, analyzed and used to calibrate a regression-based size estimation model. Forward stepwise MLR (Multiple Linear Regression) is used for model fitting. The model thus obtained is assessed using a variety of accuracy measures such as MMRE (Mean Magnitude of Relative Error), Prediction of x(PRED(x)), MdMRE (Median of Relative Error) and validated using K-fold cross validation. The accuracy of this model is also compared with an existing model tailored for size estimation of board
games. Based on a small subset of desktop games developed in various object-oriented languages, we obtained a model using CK metrics and forward stepwise multiple linear regression with reasonable estimation accuracy as indicated by the value of the coefficient of determination (R2 = 0.756).Comparison results indicate that the existing size estimation model outperforms the model derived using CK metrics in terms of accuracy of prediction.