COMPARISION OF MAINTENANCE ACTIVITY FOR EFFORT ESTIMATION IN OPEN SOURCE SOFTWARE PROJECTS

AVNEET KAUR

Abstract


Software estimation accuracy is amongst the biggest challenges for software developers. The most significant activity in software project management is Software development effort prediction. Many models have been proposed to make software effort estimations, but still no single model can predict the effort accurately. The demand for accurate effort estimation in the software industry is still a challenge. Accurate, precise and reliable estimates of effort at early stages of project development hold great importance for the management to meet the competitive demands of today's world. Software cost estimation is one of the most crucial tasks and predicts the effort and development time needed to develop a software system. It helps the software industries to manage their software development process efficiently. In this paper, we introduce an approach to building an effort estimation model for Open Source Software. For this purpose, effort data is mined from the developer's bug fix activities history. Our approach determines the actual time spend to fix a bug and considers it as an estimated effort. We propose an artificial neural-network-based approach to predict the amount of effort and development time of developers required for bug resolution. This paper investigates the use of Back-Propagation Neural networks for software effort estimation. The primary purpose of this paper is to estimate the software development effort using Artificial Neural-Network based techniques to improve accuracy of developers for bug resolution.

Keywords


Software effort estimation, Artificial neural networks, Back-propagation neural networks, Accuracy, Prediction

Full Text:

PDF

References


R. Charette, "Why Software fails [software failure]," in IEEE Spectrum, 42(9), 2005, pp. 42–49.

J. Lee, W. Lee, J-Y Kuo, “Fuzzy Logic as a Basic for Use Case Point Estimation,” in IEEE International Conference on Fuzzy Systems, Taipei, Taiwan, June 27-30, 2011, pp. 2707-2707.

K. Hamdan, M. Madi, "Software Project Effort: Different Methods of Estimation," in International Conference on Communications and Information Technology (ICCIT), Aqaba., 2011, pp. 15-18.

R.E. Fairley, “Recent Advances in Software Estimation Techniques,” in Proceedings of the 14th International Conference on Software Engineering., ACM, 1992, pp. 382-391.

C.J. Burgess and L. Lefley, “Can Genetic Programming Improve Software Effort Estimation? A Comparative Evaluation,” Information and Software Technology, vol. 43, no. 14, 2001, pp. 863-873.

A.R. Gray, S.G. MacDonnel, and M.J. Shepperd, “Factors Systematically Associated with Errors in Subjective Estimates of Software Development Effort: the Stability of Expert Judgment,” in Proceedings of Sixth International Software Metrics Symposium, IEEE, 1999, pp. 216-227.

C. Jones, "By Popular Demand: Software Estimating Rules of Thumb," Computer, vol. 29, no. 3, March 1996, p. 116.

M. Jørgensen, “A Review of Studies on Expert Estimation of Software Development Effort,” in Journal of Systems and Software, Volume 70 (1), 2004, pp. 37-60.

A. Heiat, “Comparison of artificial neural network and regression models for estimating software development effort,” in Journal of Information and Software Technology, Volume 44 (15), 2002, pp. 911–922.

R.T. Hughes, “An evaluation of machine learning techniques for software effort estimation,” University of Brighton, 1996.

M. Jorgerson, “Experience with accuracy of software maintenance task effort prediction models,” IEEE Transactions on Software Engineering, Volume 21 (8), 1995, pp. 674–681.

N. Karunanithi, D. Whitley, Y.K. Malaiya, "Using neural networks in reliability prediction," IEEE Software, Volume 9 (4), 1992, pp. 53-59.

C.F. Kemerer, “An empirical validation of software cost estimation models,” Communications of the ACM, Volume 30 (5), 1987, pp. 416–429.

B. Samson, D. Ellison, P. Dugard, “Software cost estimation using an Albus perceptron (CMAC),” in Journal of Information and Software Technology, Volume 39 (1), 1997, pp. 55–60.

C. Schofield, “Non-algorithmic effort estimation techniques,” Technical Report TR98-01, 1998.

C. Seluca, “An investigation into software effort estimation using a back propagation neural network,” M.Sc.Thesis, Bournemouth University, UK, 1995.

K. Srinivasan, D. Fisher, “Machine learning approaches to estimating software development effort,” IEEE Transactions on Software Engineering, Volume 21 (2), 1995, pp. 126–137.

G. Wittig, G. Finnie, “Estimating software development effort with connectionist models,” in Journal of Information and Software Technology, Volume 39 (7), 1997, pp. 469–476.

G.R. Finnie, G.E. Wittig, “AI tools for software development effort estimation,” in Proceedings of Conference on Software Engineering and Education and Practice, IEEE Computer Society Press, Los Alamitos, 1996, pp. 346–353.

A.R. Gray, S.G. MacDonnell, “A Comparison of Techniques for Developing Predictive Models of Software Metrics,” Information and Software Technology, Volume 39(6), 1997, pp. 425–437.

A. Lee, C.H. Cheng, J. Balakrishan, “Software Development Cost Estimation: Integrating Neural Network with Cluster Analysis,” Information and Management Volume 34(1), 1998, pp. 1–9.

K.K. Shukla, “Neuro-Genetic prediction of software development effort,” in International Journal of Information a3nd Software Technology, Volume 42(10), 2000, pp. 701–703.




DOI: https://doi.org/10.26483/ijarcs.v8i5.3680

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2017 International Journal of Advanced Research in Computer Science