This paper explores software development through early prediction of planning phase . It summarizes a variety of techniques for software planning prediction in the domain of software engineering. The objective of this research is to apply the various machine learning approaches, such as Case-Based Reasoning and Fuzzy logic, to predict software planning. The system predicts the planning phase activity after accepting the values of certain parameters of the software. This paper advocates the use of case-based reasoning (i.e., CBR) to build a software development prediction system with the help of human experts. The prediction is based on analogy. We have used different similarity measures to find the best method that increases reliability. It can be readily deployed on any configuration without affecting its performance.

Software Engineeering,SDLC Model,Machine Learning, CBR

[1] G. Kadoda, M Cartwright, L Chen, and M. Shepperd. (2000), “Experiences Using Case- Based Reasoning to Predict Software Project Effort”, In Proceeding of EASE, p. 23-28, Keele, UK.
[2] I. Myrtveit and E. Stensrud. (1999), “A Controlled Experiment to Assess the Benefits of Estimating with Analogy and Regression Models”, IEEE transactions on software Engineering, Vol 25, no. 4, pp. 510-525.
[3] K. Ganeasn, T.M. Khoshgoftaar, and E. Allen. (2000), “Case-based Software Quality Prediction”, International journal of Software Engineering and Knowledge Engineering, 10 (2), pp. 139-152 .
[4] Shi Zhong,Taghi M.Khoshgoftaar and Naeem Selvia “Unsupervised Learning for Expert-Based Software Quality Estimation”.Proceeding of the Eighth IEEE International Symposium on High Assurance Systems Engineering (HASE’04).
[5] Ekbal Rashid, Srikanta Patnaik, Vandana Bhattacherjee “A Survey in the Area of Machine Learning and Its Application for Software Quality Prediction” has been published in ACM SigSoft ISSN 0163-5948, volume37,number5,September2012,http://doi.acm.org/10.1145/2347696.2347709 New York, NY, USA.
[6] M. J. Khan, S. Shamail, M. M Awais, and T. Hussain, “ Comparative study of various artificial intelligence techniques to predict software quality” in proceedings of the 10th IEEE multitopic conference, 2006, INMIC 06, PP 173-177, Dec 2006.
[7] S. Becker, L. Grunske, R. Mirandola, and S. Overhage, “ Performance prediction of component-based systems a survey from an engineering perspective”, In architechture systems with Trust-worthy components, Vol 3938 of LNCS, Springer, 2006.
[8] Ekbal Rashid, Srikanta Patnaik, Vandana Bhattacherjee “Enhancing the accuracy of case-based estimation model through Early Prediction of Error Patterns” proceedings published by the IEEE Computer Society 10662 Los Vaqueros Circle Los Alamitos, CA, in International Symposium on Computational and Business Intelligence (ISCBI 2013), New Delhi, 24~26 Aug 2013 ISBN 978-07695-5066-4/13 IEEE, DOI 10.1109/ISCBI.2013.
[9] Aamodt, A. and E. Plaza, Case-based reasoning: foundational issues, methodical variations and system approaches. AI Communications 7(1), 1994.
[10] Venkata U.B.Challagulla et al ”A Unified Framework for Defect data analysis using the MBR technique”. Proceeding of the 18th IEEE International Conference on Tools with Artificial Intelligence(ICTAI’06).
[11] Tom M. Mitchell, “Machine LearningSection 4.1.1; page 82, McGraw Hill Companies, Inc. (1997).
[12] David E. Goldberg “Genetic Algorithms in search, Optimization and Machine learning” Pearson Education, Inc.
[13] Luger, George F. Artificial Intelligence, Structures and Strategies for Complex Problem Solving, Fourth Edition, atpage 471 2002. Harlow, England: Addison-Wesley.
[14] Ekbal. Rashid "R4 Model for Case-Based Reasoning and Its Application for Software Fault Prediction," International Journal of Software Science and Computational Intelligence (IJSSCI) 8 (2016): 3, doi:10.4018/IJSSCI.2016070102.
[15] Ekbal Rashid “Improvisation of Case-Based Reasoning and Its Application for Software Fault Prediction” has been published in International Journal of Services Technology and Management (IJSTM).ISSN online: 1741-525X ISSN print: 1460-6720, Vol.21, No.4/5/6, pp.214,227,DOI:http://dx.doi.org/10.1504/IJSTM.2015.073921, Inderscience Publisher.
[16] Catal C. Software mining and fault prediction. WIREs Data Mining Knowl Discov 2012; 2: 420-426.