[1] Basha, S., & Ponnurangam, D. (2010). Analysis of empirical software effort estimation models. arXiv preprint arXiv:1004.1239.
[2] Baskeles, B., Turhan, B., & Bener, A. (2007). Software effort estimation using machine learning methods. In 2007 22nd international symposium on computer and information sciences (pp. 1-6). IEEE.
[3] Boehm, B., Clark, B., Horowitz, E., Westland, C., Madachy, R., & Selby, R. (1995). Cost models for future software life cycle processes: COCOMO 2.0. Annals of software engineering, 1(1), pp 57-94.
[4] Boetticher, G. (2001). Using machine learning to predict project effort: Empirical case studies in data-starved domains. In First international workshop on model-based requirements engineering.
[5] Eberendu, A. C. (2014). Software Project Cost Estimation: Issues, Problems and Possible Solutions. International Journal of Engineering Science Invention, Vol 3, Issue 6, pp 38-43.
[6] Galinina, A., Burceva, O., & Parshutin, S. (2012). The optimization of COCOMO Model coefficients using Genetic Algorithms. Information Technology and Management Science, 15(1), pp 45-51.
[7] Heetika Duggal, P. S. (2012). Comparative Study of the Performance of M5-Rules Algorithm with Different Algorithms. Journal of Software Engineering and Applications, pp 270-276.
[8] Rekha Tripathi, D. P. (January 2016). Comparative Study of Software Cost Estimation Techniques. International Journal of Advanced Research in Computer Science and Software Engineering, Vol 6, Issue 1, pp 323-328.
[9] Rshma Chawla, D. A. (2014). Software Development Effort Estimation Techniques: A Review. International Journal of Electronics Communication and Computer Engineering,Vol 5, Issue 5, pp 1166-1170.
[10] Wen, J., Li, S., Lin, Z., Hu, Y., & Huang, C. (2012). Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology, Vol 54, Issue 1, pp 41-59.
[11] Sarro, F., Petrozziello, A., & Harman, M. (2016). Multi-objective software effort estimation. In 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE). pp 619-630.
[12] Braga, P. L., Oliveira, A. L., & Meira, S. R. (2007). Software effort estimation using machine learning techniques with robust confidence intervals. In 7th international conference on hybrid intelligent systems (HIS 2007). pp 352-357.
[13] Malhotra, R., & Jain, A. (2011). Software effort prediction using statistical and machine learning methods. International Journal of Advanced Computer Science and Applications, Vol 2, Issue 1, pp 145-152