• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

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

Original Article

Predictive analytics approaches for software effort estimation: A review

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


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 


  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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


© 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)


Subscribe now for latest articles and news.