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A Systematic Literature Review on Effort Estimation in Agile Software Development using Machine Learning Techniques

by Pranay Tandon, Ugrasen Suman, Maya Rathore
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 21
Year of Publication: 2022
Authors: Pranay Tandon, Ugrasen Suman, Maya Rathore
10.5120/ijca2022922238

Pranay Tandon, Ugrasen Suman, Maya Rathore . A Systematic Literature Review on Effort Estimation in Agile Software Development using Machine Learning Techniques. International Journal of Computer Applications. 184, 21 ( Jul 2022), 15-23. DOI=10.5120/ijca2022922238

@article{ 10.5120/ijca2022922238,
author = { Pranay Tandon, Ugrasen Suman, Maya Rathore },
title = { A Systematic Literature Review on Effort Estimation in Agile Software Development using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2022 },
volume = { 184 },
number = { 21 },
month = { Jul },
year = { 2022 },
issn = { 0975-8887 },
pages = { 15-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number21/32440-2022922238/ },
doi = { 10.5120/ijca2022922238 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:22:03.118105+05:30
%A Pranay Tandon
%A Ugrasen Suman
%A Maya Rathore
%T A Systematic Literature Review on Effort Estimation in Agile Software Development using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 21
%P 15-23
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Agile software development is a way of frequent or continuous delivery of software. Nowadays many software industries have adopted agile for software development. The predictability and stability of traditional methods were replaced with flexibility, adaptability and agility to generate maximum value with collaboration and interaction, as quickly as possible. Effort estimation is the focused area in agile software development to achieve customer collaboration, respond to change and deliver a working software on time. Machine learning is an advanced tool to obtain effort estimation with available project data and widely used in IT industries to get accurate estimations. In this paper, the findings are reported through systematic literature review that aimed at identifying the applicability, limitations and individual result of most used machine learning techniques for effort estimation in agile software development with the help of 3 research questions. Also, suggested attributes of a robust machine learning model are discussed to achieve more accurate effort estimation. Conclusion of paper can help researchers and IT consultants in building a ML model considering the applicability, results and limitations of ML techniques.

References
  1. Pospieszny, P., Czarnacka-Chrobot, B., & Kobylinski, A. (2017). An effective approach for software project effort and duration estimation with machine learning algorithms. Journal of Systems and Software.
  2. 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(1), pp. 41–59.
  3. Moløkken-Østvold, K. (2004). A survey on software estimation in the Norwegian industry. Proceeding International Software Metrics Symposium, pp. 208-219.
  4. Shepperd, M. & Schofield, C. (1996). Estimating software project effort using analogies. IEEE Transactions on Software Engineering, Vol. 23(11), pp. 736-743.
  5. Panda, A., Satapathy, S.M. & Rath, S.K. (2015). Neural Network Models for Agile Software Effort Estimation based on Story Points. International Journal of Artificial Intelligence and Neural Networks IJAINN, Vol. 5, Issue 2.
  6. Malgonde, O. & Chari, K. (2018). An ensemble-based model for predicting agile software development effort. Springer Science+Business Media, LLC, part of Springer Nature.
  7. Moharreri, K., Sapre, A.V., Ramanathan, J. & Ramnath, R. (2016). Cost-Effective Supervised Learning Models for Software Effort Estimation in Agile Environments. IEEE 40th Annual Computer Software & Applications Conference.
  8. Soares, R.G.F. (2018). Effort estimation via text classification and autoencoders. International Joint Conference on Neural Networks (IJCNN).
  9. Satapathy, S.M., & Rath, S.K. (2017). Empirical assessment of machine learning models for agile software development effort estimation using story points. Springer-Verlag London Ltd.
  10. Hammad, M., & Alqaddoumi, A. (2018). Features-Level Software Effort Estimation Using Machine Learning Algorithms. International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT).
  11. Ionescu, V.S., Demian , H., & Czibula, I.G. (2017). Natural Language Processing and Machine Learning Methods for Software Development Effort Estimation. Studies in Informatics and Control, 26(2) 219-228.
  12. Sharma, P., & Singh, J. (2017). Systematic Literature Review on Software Effort Estimation Using Machine Learning Approaches. International Conference on Next Generation Computing & Information Systems (ICNGCIS).
  13. Ionescu, V.S. (2017). An approach to software development effort estimation using machine learning. IEEE.
  14. Radu, L.D. (2019). Effort Prediction in Agile Software Development with Bayesian Networks. 14th International Conference on Software Technologies (ICSOFT 2019), pages 238-245.
  15. Zakrani, A., Nazm , A. & Marzak, A. (2018). Support Vector Regression Based on Grid Search Method for Agile Software Effort Prediction. IEEE.
  16. Rao, C.P., Kumar, P.S., Sree, S. R., & Devi, J. (2018). An Agile Effort Estimation Based on Story Points Using Machine Learning Techniques. Second International Conference on Computational Intelligence and Informatics, Advances in Intelligent Systems and Computing 712.
  17. Sree, S. R., Rao, C.P., & Mounika, M. (2017). An Early-Stage Software Effort Estimation in Agile Methodology Based On User Stories Using Machine Learning Techniques. International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:6.887 Vol. 5 Issue XII.
  18. Pospieszny, P., Czarnacka-Chrobot, B. & Kobylinski, A. (2018). An effective approach for software project effort and duration estimation with machine learning algorithms. The Journal of Systems and Software 137 (2018) 184–196.
  19. Srdjana, D., Stipe , C., & Mili, T. (2017). Bayesian Network Model for Task Effort Estimation in Agile Software Development. The Journal of Systems & Software.
  20. Premalatha, H.M., & Srikrishna , C.V. (2019). Effort Estimation in Agile Software Development using Evolutionary Cost- Sensitive Deep Belief Network. International Journal of Intelligent Engineering and Systems, Vol.12, No.2.
  21. Bilgaiyan, S., Mishra, S. & Das, M. (2018). Effort estimation in agile software development using experimental validation of neural network models. Springer.
  22. Banimustafa, A. (2018). Predicting Software Effort Estimation Using Machine Learning Techniques. 8th International Conference on Computer Science and Information Technology (CSIT).
  23. Nassif, A.B., Azzeh, M., Capretz , L.F., & Ho, D. (2015). Neural Network Models for Software Development Effort Estimation: A Comparative Study. The Natural Computing Applications Forum.
  24. Rijwani, P., & Jain, S. (2016). Enhanced Software Effort Estimation using Multi Layered Feed Forward Artificial Neural Network Technique. Twelfth International Multi-Conference on Information Processing (IMCIP).
  25. Phannachitta, P., & Matsumoto, M. (2019). Model-Based Software Effort Estimation – A Robust Comparison of 14 Algorithms Widely Used in The Data Science Community. International Journal of Innovative Computing, Information and Control Vol. 15, Number 2.
  26. Oliveira, A.L.I., Braga, P.L., Lima, R.M.F., & Cornelio, M.L. (2010). Genetic Algorithm-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation. Information and Software Technology 52 (2010) 1155–1166.
  27. Finnie, G. R., Wittig, G. E., & Desharnais, J.M. (1997). A Comparison of Software Effort Estimation Techniques: Using Function Points with Neural Networks, Case-Based Reasoning and Regression Models. Elsevier.
  28. Choetkiertikul, M., Dam, H. K., Tran, T., Pham, T., Ghose, A., & Menzies, T. (2015). A deep learning model for estimating story points”, Journal of latex class files, Vol. 14, NO. 8, August 2015.
  29. Alsubhi, K.A. (2019). Effort Estimation in Agile Software Development Using Deep Learning Model. International Journal of Advances in Computer Science and Technology, Volume 8..
  30. Kumar, P.S., Behera, H.S., Kumari K., A., Nayak, J., & Naik, B. (2020). Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades. Computer Science Review 38 (2020) 100288.
  31. Kichenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. Vol. 2.3 EBSE Technical Report, EBSE-2007-01. Software Engineering Group, School of Computer Science and Mathematics, Keele University, Keele, UK.
  32. Bilgaiyan, s., Mishra, s., & Das, M. (2016). A Review of Software Cost Estimation in Agile Software Development Using Soft Computing Techniques. IEEE 2016 2nd International Conference on Computational Intelligence and Networks (CINE) - Bhubaneswar, India.
Index Terms

Computer Science
Information Sciences

Keywords

Agile software development effort estimation machine learning systematic literature review techniques methods limitations model deep learning.

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