Predictive analytics approaches for software effort estimation: A review

A G Priya Varshini  lowast; K Anitha Kumari

doi:10.17485/IJST/v13i21.573

Article

Predictive analytics approaches for software effort estimation: A review

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v13i21.573

Year: 2020, Volume: 13, Issue: 21, Pages: 2094-2103

Original Article

Predictive analytics approaches for software effort estimation: A review

A G Priya Varshini^1∗, K Anitha Kumari²

1 Assistant Professor, Information Technology, Dr.Mahalingam College of Engineering and Technology, Pollachi, Coimbatore, India. Tel.: +91-9444899863
2 Associate Professor, Information Technology, PSG College of Technology, Coimbatore, India

∗Corresponding author:
A G Priya Varshini
Assistant Professor, Information Technology, Dr.Mahalingam College of Engineering and Technology, Pollachi, Coimbatore, India.
Tel: +91-9444899863
Email: [email protected]

Received Date:10 May 2020, Accepted Date:10 June 2020, Published Date:22 June 2020

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objective: In Software Effort Estimation (SEE), predicting the amount of time taken in human hours or months for software development is considered as a cumbersome process. SEE consists of both Software Development Effort Estimation (SDEE) and Software Maintenance Effort Estimation (SMEE). Over estimation or under estimation of software effort results in project cancellation or project failure. The objective of this study is to identify the best performing model for software Effort Estimation through experimental comparison with various Machine learning algorithms. Methods: Software Effort Estimation was addressed by using various machine learning techniques such as Multilinear Regression, Ridge Regression, Lasso Regression, ElasticNet Regression, Random Forest, Support Vector Machine, Decision Tree and NeuralNet to recognize best performing model. Datasets used are Desharnais, Maxwell, China and Albrecht datasets. Evaluation metrics considered are Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE) and R-Squared. Findings: Experiments on various machine learning algorithms for software Effort Estimation determines that Support Vector Machine produced the best performance comparatively with other algorithms.

Keywords: Machine learning; software effort estimation; regression models; classification models

References

Ali A, Gravino C. A systematic literature review of software effort prediction using machine learning methods. Journal of Software: Evolution and Process. 2019;31(10). Available from: https://dx.doi.org/10.1002/smr.2211
Pospieszny P, Czarnacka-Chrobot B, Kobylinski A. An effective approach for software project effort and duration estimation with machine learning algorithms. Journal of Systems and Software. 2018;137:184–196. Available from: https://dx.doi.org/10.1016/j.jss.2017.11.066
Jørgensen M. Unit effects in software project effort estimation: Work-hours gives lower effort estimates than workdays. Journal of Systems and Software. 2016;117:274–281. Available from: https://doi.org/doi:10.1016/j.jss.2016.03.048
Conoscenti M, Besner V, Vetrò A, Fernández DM. Combining data analytics and developers feedback for identifying reasons of inaccurate estimations in agile software development. Journal of Systems and Software. 2019;156:126–135. Available from: https://dx.doi.org/10.1016/j.jss.2019.06.075
Idri A, Amazal Fa, Abran A. Analogy-based software development effort estimation: A systematic mapping and review. Information and Software Technology. 2015;58:206–230. Available from: https://dx.doi.org/10.1016/j.infsof.2014.07.013
Benala TR, Mall R. DABE: Differential evolution in analogy-based software development effort estimation. Swarm and Evolutionary Computation. 2018;38:158–172. Available from: https://dx.doi.org/10.1016/j.swevo.2017.07.009
Silhavy P, Silhavy R, Prokopova Z. Categorical Variable Segmentation Model for Software Development Effort Estimation. IEEE Access. 2019;7:9618–9626. Available from: https://dx.doi.org/10.1109/access.2019.2891878
Martino SD, Ferrucci F, Gravino C, Sarro F. Web Effort Estimation: Function Point Analysis vs. COSMIC. Information and Software Technology. 2016;72:90–109. Available from: https://dx.doi.org/10.1016/j.infsof.2015.12.001
Effendia A, Setiawan R, Rasjid ZE. Adjustment Factor for Use Case Point Software Effort Estimation (Study Case: Student Desk Portal) In: Budiharto W., ed. The 4th International Conference on Computer Science and Computational Intelligence (ICCSCI 2019) : Enabling Collaboration to Escalate Impact of Research Results for Society. 157:691–698.
Azzeh M, Nassif AB, Banitaan S. Comparative analysis of soft computing techniques for predicting software effort based use case points. IET Software. 2018;12(1):19–29. Available from: https://dx.doi.org/10.1049/iet-sen.2016.0322
Oliveira ALI, Braga PL, Lima RMF, Cornélio ML. GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation. Information and Software Technology. 2010;52(11):1155–1166. Available from: https://dx.doi.org/10.1016/j.infsof.2010.05.009
González-Ladrón-de-Guevara F, Fernández-Diego M, Lokan C. The usage of ISBSG data fields in software effort estimation: A systematic mapping study. Journal of Systems and Software. 2016;113:188–215. Available from: https://dx.doi.org/10.1016/j.jss.2015.11.040
Mensah S, Keung J, Bosu MF, Bennin KE. Duplex output software effort estimation model with self-guided interpretation. Information and Software Technology. 2018;94:1–13. Available from: https://dx.doi.org/10.1016/j.infsof.2017.09.010
Wen J, Li S, Lin Z, Hu Y, Huang C. Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology. 2012;54:41–59. Available from: https://dx.doi.org/10.1016/j.infsof.2011.09.002
García-Floriano A, López-Martín C, Yáñez-Márquez C, Abran A. Support vector regression for predicting software enhancement effort. Information and Software Technology. 2018;97:99–109. Available from: https://dx.doi.org/10.1016/j.infsof.2018.01.003
Zare F, Zare HK, Fallahnezhad MS. Software effort estimation based on the optimal Bayesian belief network. Applied Soft Computing. 2016;49:968–980. Available from: https://dx.doi.org/10.1016/j.asoc.2016.08.004
abdelali Z, Mustapha H, Abdelwahed N. Investigating the use of random forest in software effort estimation. Procedia Computer Science. 2019;148:343–352. Available from: https://dx.doi.org/10.1016/j.procs.2019.01.042
Satapathy SM, Rath SK, Acharya BP. Early stage software effort estimation using random forest technique based on use case points. IET Software. 2016;10(1):10–17. Available from: https://dx.doi.org/10.1049/iet-sen.2014.0122
Nassif AB, Ho D, Capretz LF. Towards an early software estimation using log-linear regression and a multilayer perceptron model. Journal of Systems and Software. 2013;86(1):144–160. Available from: https://dx.doi.org/10.1016/j.jss.2012.07.050
Rijwani P, Jain S. Enhanced Software Effort Estimation Using Multi Layered Feed Forward Artificial Neural Network Technique. Procedia Computer Science. 2016;89:307–312. Available from: https://dx.doi.org/10.1016/j.procs.2016.06.073
Araújo RdA, Oliveira ALI, Meira S. A class of hybrid multilayer perceptrons for software development effort estimation problems. Expert Systems with Applications. 2017;90:1–12. Available from: https://dx.doi.org/10.1016/j.eswa.2017.07.050
Malgonde O, Chari K. An ensemble-based model for predicting agile software development effort. Empirical Software Engineering. 2018. Available from: https://doi.org/10.1007/s10664-018-9647-0
Vijay JF. Enrichment of accurate software effort estimation using fuzzy-based function point analysis in business data analytics. Neural Computing and Applications. 2019;31(5):1633–1639. Available from: https://dx.doi.org/10.1007/s00521-018-3565-3
Ezghari S, Zahi A. Uncertainty management in software effort estimation using a consistent fuzzy analogy-based method. Applied Soft Computing. 2018;67:540–557. Available from: https://dx.doi.org/10.1016/j.asoc.2018.03.022
Keung J, Kocaguneli E, Menzies T. Finding conclusion stability for selecting the best effort predictor in software effort estimation. Automated Software Engineering. 2013;20(4):543–567. Available from: https://dx.doi.org/10.1007/s10515-012-0108-5
Minku LL, Yao X. Ensembles and locality: Insight on improving software effort estimation. Information and Software Technology. 2013;55:1512–1528. Available from: https://dx.doi.org/10.1016/j.infsof.2012.09.012
Arbain SH, Ali NA, Mustaffa NH. Adoption of Machine Learning Techniques in Software Effort Estimation: An Overview. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing. 551.
Nassif AB, Azzeh M, Idri A, Abran A. Software Development Effort Estimation Using Regression Fuzzy Models. Computational Intelligence and Neuroscience. 2019;2019:1–17. Available from: https://dx.doi.org/10.1155/2019/8367214

Copyright

© 2020 Priya Varshini, Anitha Kumari. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Published By Indian Society for Education and Environment (iSee)