CFP last date
20 January 2025
Reseach Article

Towards a Machine Learning Model for Predicting Failure of Agile Software Projects

by Ahmed Abdelaziz Mohamed, Nagy Ramadan Darwish, Hesham Ahmed Hefny
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 168 - Number 6
Year of Publication: 2017
Authors: Ahmed Abdelaziz Mohamed, Nagy Ramadan Darwish, Hesham Ahmed Hefny
10.5120/ijca2017914466

Ahmed Abdelaziz Mohamed, Nagy Ramadan Darwish, Hesham Ahmed Hefny . Towards a Machine Learning Model for Predicting Failure of Agile Software Projects. International Journal of Computer Applications. 168, 6 ( Jun 2017), 20-26. DOI=10.5120/ijca2017914466

@article{ 10.5120/ijca2017914466,
author = { Ahmed Abdelaziz Mohamed, Nagy Ramadan Darwish, Hesham Ahmed Hefny },
title = { Towards a Machine Learning Model for Predicting Failure of Agile Software Projects },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 168 },
number = { 6 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume168/number6/27879-2017914466/ },
doi = { 10.5120/ijca2017914466 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:15:25.101748+05:30
%A Ahmed Abdelaziz Mohamed
%A Nagy Ramadan Darwish
%A Hesham Ahmed Hefny
%T Towards a Machine Learning Model for Predicting Failure of Agile Software Projects
%J International Journal of Computer Applications
%@ 0975-8887
%V 168
%N 6
%P 20-26
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Agile software development plays a very significant role in software projects. Agile software project is a refined approach to design and direct project processes. An agile project is finished in short sections called iterations. This paper introduces a survey of machine learning approaches for predicting failure of agile software projects. It reviews the uses of machine learning techniques such as fuzzy logic, multiple linear regression, neural network, logistic regression and etc., for predicting success and failure of agile software projects. This paper also proposes machine learning model for predicting failure of agile software projects. Many researches in this topic were reviewed, analyzed, summarized, and compared according to the used machine learning techniques in agile software projects.

References
  1. V. Lalsing, S. Kishnah and S. Pudaruth “People Factors in Agile Software Development and Project Management”, IJSEA, Vol.3, No.1, 2012, pp. 117-137.
  2. J. Li “Agile Software Development”, Technics University Berlin, Berlin, Germany, 2012.
  3. John Hunt, “Agile software construction”, Springer, 2006.
  4. Ann L. Fruhling and Alvin E. Tarrell, “Best Practices for Implementing Agile Methods”, IBM, 2008.
  5. Laurie Williams, Robert R. Kessler, Ward Cunningham, and Ron Jeffries, “Strengthening the case for pair Programming”, IEEE Software, 2000.
  6. Mike Cohn, “Software Development using Scrum”, Addison-Wesley, 2010.
  7. K. N. Rao, G. K. Naidu and P. Chakka “A Study of the Agile Software Development Methods, Applicability and Implications in Industry”, IJSEA, Vol.5, No.2, 2011, pp. 35-46.
  8. Ioannis G. Stamelos and P. Sfetsos “Agile Software Development Quality Assurance”, information science references, Idea Group Inc, 2007.
  9. V. E. Jyothi and K. N. Rao “Effective Implementation of Agile Practices”, IJACSA, Vol.2, No.3, 2011, pp. 41-48.
  10. ELZAMLY and B. HUSSIN “An Enhancement of Framework Software Risk Management Methodology for Successful Software Development”, JATIT, Vol.62, No.2, 2014, pp. 410-423.
  11. K. Jammalamadaka and V. R. Krishna “Agile Software Development and Challenges”, IJRET, Vol.2, No.8, 2013, pp. 125-129.
  12. TAHERDOOST and A. KESHAVARZSALEH “A Theoretical Review on IT Project Success/Failure Factors and Evaluating the Associated Risks”, Mathematical and Computational Methods in Electrical Engineering, vol.1, 2012, pp. 80-88.
  13. M. Shepperd and S. MacDonell “Evaluating prediction systems in software project estimation”, Information and Software Technology, ELSEVIER, vol.54, 2012, pp. 820-827.
  14. S. Lee and H. Yong “Agile Software Development Framework in a Small Project Environment”, JIPS, Vol.9, No.1, 2013, pp. 69-88.
  15. Tanner and U. Willingh “Factors Leading to the Success and Failure of Agile Projects Implemented in Traditionally Waterfall Environments”, MKL, 2014, pp. 693-701.
  16. Feras A. Batarseh and Avelino J. Gonzalez “Predicting failures in agile software development through data analytics”, Springer, 2015.
  17. D. S. Nguyen “Success Factors That Influence Agile Software Development Project Success”, ASRJETS, vol.17, No 1, 2016, pp. 172-222.
  18. T. Chow, D. Cao “A survey study of critical success factors in agile software projects”, JSS, Elsevier, vol.81, 2008, pp. 961-971.
  19. N. Cerpa, M. Bardeen, B. Kitchenham and J. Verner “Evaluating logistic regression models to estimate software project outcomes”, IST, Elsevier, vol.52, 2010, pp. 934-944.
  20. R. P. Mohanty, G. Sahoo and J. Dasgupta “Identification of Risk Factors in Globally Outsourced Software Projects using Logistic Regression and ANN”, Int. J Sup. Chain. Mgt, vol.1, 2012, pp. 2-11.
  21. D. Stankovic, V. Nikolicb, M. Djordjevicc, D. Caod “A survey study of critical success factors in agile software projects in former Yugoslavia IT companies”, JSS, Elsevier, vol.86, 2013, pp. 1663– 1678.
  22. Pushpavathi T.P, Suma V, and Ramaswamy V “Defect Prediction in Software Projects-Using Genetic Algorithm based Fuzzy C-Means Clustering and Random Forest Classifier”, IJSER, Vol.5, 2014, pp. 888-898.
  23. H. Yadav, D. Yadav “A fuzzy logic based approach for phase-wise software defects Prediction using software metrics”, INFSOF, 2015, vol.1, pp. 1-19.
  24. T. Hovorushchenko and A. Krasiy “Realization of the Neural Network Model of Prediction of the Software Project Characteristics for Evaluating the Success of its Implementation”, ICIDAACS, IEEE, 2015, pp. 348-353.
  25. S. A. Rizvi, R. A. Khan and V. K. Singh “Software Reliability Prediction using Fuzzy Inference System: Early Stage Perspective”, IJCA, vol.145, No 10, 2016, pp. 16-23.
  26. V. Vashisht, M. Lal and G. S. Sureshchandar “Defect Prediction Framework Using Adaptive Neuro-Fuzzy Inference System (ANFIS) for Software Enhancement Projects”, British Journal of Mathematics & Computer Science, vol.19, No 2, 2016, pp. 1-12.
  27. N. R. Darwish, A. A. Mohamed and A. S. Abdelghany “A Hybrid Machine Learning Model for Selecting Suitable Requirements Elicitation Techniques”, IJCSIS, vol.14, No 6, 2016, pp. 1-12.
  28. A. A. Mohamed and A. S. Salama “A Fuzzy Logic based Model for Predicting Commercial Banks Financial Failure”, IJCA, vol.79, No 11, 2013, pp. 16-21.
Index Terms

Computer Science
Information Sciences

Keywords

Agile Software Projects Machine Learning Fuzzy Logic Multiple Linear Regression.