SpringerLink
Log in

Empirical assessment of machine learning models for agile software development effort estimation using story points

  • Original Paper
  • Published:
Innovations in Systems and Software Engineering Aims and scope Submit manuscript

Abstract

In the present day develo** houses, the procedures adopted during the development of software using agile methodologies are acknowledged as a better option than the procedures followed during conventional software development due to its innate characteristics such as iterative development, rapid delivery and reduced risk. Hence, it is desirable that the software development industries should have proper planning for estimating the effort required in agile software development. The existing techniques such as expert opinion, analogy and disaggregation are mostly observed to be ad hoc and in this manner inclined to be mistaken in a number of cases. One of the various approaches for calculating effort of agile projects in an empirical way is the story point approach (SPA). This paper presents a study on analysis of prediction accuracy of estimation process executed in order to improve it using SPA. Different machine learning techniques such as decision tree, stochastic gradient boosting and random forest are considered in order to assess prediction more qualitatively. A comparative analysis of these techniques with existing techniques is also presented and analyzed in order to critically examine their performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Abrahamsson P, Fronza I, Moser R, Vlasenko J, Pedrycz W (2011) Predicting development effort from user stories. In: Empirical software engineering and measurement (ESEM), 2011 international symposium on, IEEE, pp 400–403

  2. Azzeh M, Nassif AB, Minku LL (2015) An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation. J Syst Softw 103:36–52

    Article  Google Scholar 

  3. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  4. Britto R, Mendes E, Borstler J (2015) An empirical investigation on effort estimation in agile global software development. In: Global Software Engineering (ICGSE), 2015 IEEE 10th International Conference on. IEEE, IEEE, pp 38–45

  5. Coelho E, Basu A (2012) Effort estimation in agile software development using story points. Int J Appl Inf Syst (IJAIS) 3(7)

  6. Cohen D, Lindvall M, Costa P (2004) An introduction to agile methods. Adv Comput 62:1–66

    Article  Google Scholar 

  7. Foss T, Stensrud E, Kitchenham B, Myrtveit I (2003) A simulation study of the model evaluation criterion mmre. IEEE Trans Softw Eng 29(11):985–995

    Article  Google Scholar 

  8. Fowler M, Highsmith J (2001) The agile manifesto. Softw Dev 9(8):28–35

    Google Scholar 

  9. Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378

    Article  MathSciNet  MATH  Google Scholar 

  10. Garg S, Gupta D (2015) PCA based cost estimation model for agile software development projects. In: Industrial Engineering and Operations Management (IEOM), 2015 International Conference on, IEEE, pp 1–7

  11. Grapenthin S, Poggel S, Book M, Gruhn V (2014) Facilitating task breakdown in sprint planning meeting 2 with an interaction room: an experience report. In: Software engineering and advanced applications (SEAA), 2014 40th EUROMICRO Conference on, IEEE, pp 1–8

  12. Hamouda AED (2014) Using agile story points as an estimation technique in cmmi organizations. In: Agile conference (AGILE), 2014, IEEE, pp 16–23

  13. Hearty P, Fenton N, Marquez D, Neil M (2009) Predicting project velocity in xp using a learning dynamic bayesian network model. IEEE Trans. Softw. Eng. 35(1):124–137

    Article  Google Scholar 

  14. Hussain I, Kosseim L, Ormandjieva O (2013) Approximation of cosmic functional size to support early effort estimation in agile. Data Knowl Eng 85:2–14

    Article  Google Scholar 

  15. Kang S, Choi O, Baik J (2010) Model-based dynamic cost estimation and tracking method for agile software development. In: Computer and information science (ICIS), 2010 IEEE/ACIS 9th International Conference on, IEEE, pp 743–748

  16. Keaveney S, Conboy K (2006) Cost estimation in agile development projects. In: ECIS, pp 183–197

  17. Kitchenham B, Pickard L, MacDonell S, Shepperd M (2001) What accuracy statistics really measure [software estimation]. IEE Proc Softw 148(3):81–85. doi:10.1049/ip-sen:20010506

    Article  Google Scholar 

  18. Lenarduzzi V, Lunesu I, Matta M, Taibi D (2015) Functional size measures and effort estimation in agile development: a replicated study. In: Agile processes, in software engineering, and extreme programming. Springer, Berlin, pp 105–116

  19. Mahnic V (2011) A case study on agile estimating and planning using scrum. Elektron ir Elektrotech 111(5):123–128

    Article  Google Scholar 

  20. Mahnic V, Zabkar N (2012) Measuring progress of scrum-based software projects. Elektron ir Elektrotech 18(8):73–76

    Article  Google Scholar 

  21. Menzies T, Chen Z, Hihn J, Lum K (2006) Selecting best practices for effort estimation. IEEE Trans Softw Eng 32(11):883–895

    Article  Google Scholar 

  22. Moreira ME (2013) Working with story points, velocity, and burndowns. In: Being Agile, Springer, Berlin, pp 187–194

  23. Morgan JN, Messenger RC (1973) THAID: a sequential analysis program for the analysis of nominal scale dependent variables. Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor

  24. Morgan JN, Sonquist JA (1963) Problems in the analysis of survey data, and a proposal. J Am Stat Assoc 58(302):415–434

    Article  MATH  Google Scholar 

  25. Nassif AB, Capretz LF, Ho D, Azzeh M (2012) A treeboost model for software effort estimation based on use case points. In: Machine learning and applications (ICMLA), 2012 11th international conference on, IEEE, vol 2, pp 314–319

  26. Nassif AB, Azzeh M, Capretz LF, Ho D (2013a) A comparison between decision trees and decision tree forest models for software development effort estimation. In: Communications and Information Technology (ICCIT), 2013 Third International Conference on, IEEE, pp 220–224

  27. Nassif AB, Ho D, Capretz LF (2013b) Towards an early software estimation using log-linear regression and a multilayer perceptron model. J Syst Softw 86(1):144–160

    Article  Google Scholar 

  28. Popli R, Chauhan N (2014) Cost and effort estimation in agile software development. In: Optimization, reliability, and information technology (ICROIT), 2014 international conference on, IEEE, pp 57–61

  29. Raslan AT, Darwish NR, Hefny HA (2015) Towards a fuzzy based framework for effort estimation in agile software development. Int J Comput Sci Inf Secur 13(1):37

    Google Scholar 

  30. Satapathy SM, Acharya BP, Rath SK (2014a) Class point approach for software effort estimation using stochastic gradient boosting technique. ACM SIGSOFT Softw Eng Notes 39(3):1–6

    Article  Google Scholar 

  31. Satapathy SM, Panda A, Rath SK (2014b) Story point approach based agile software effort estimation using various svr kernel methods. In: The twenty-sixth international conference on software engineering and knowledge engineering, SEKE, pp 304–307

  32. Satapathy SM, Acharya BP, Rath SK (2016) Early stage software effort estimation using random forest technique based on use case points. IET Softw 10(1):10–17

    Article  Google Scholar 

  33. Schmietendorf A, Kunz M, Dumke R (2008) Effort estimation for agile software development projects. In: 5th Software measurement European Forum, pp 113–123

  34. Schweighofer T (2016) How is effort estimated in agile software development projects? In: Fifth workshop on software quality analysis, monitoring, improvement, and applications SQAMIA 2016, p 73

  35. Shepperd M, MacDonell S (2012) Evaluating prediction systems in software project estimation. Inf Softw Technol 54(8):820–827

    Article  Google Scholar 

  36. Sobiech F, Eilermann B, Rausch A (2016) Using synergies between user stories in scrum. Lect Notes Softw Eng 4(2):91

    Article  Google Scholar 

  37. Tanveer B (2016) Hybrid effort estimation of changes in agile software development. In: International conference on agile software development. Springer, Berlin, pp 316–320

  38. Trendowicz A, Jeffery R (2014) Software project effort estimation. In: Foundations and best practice guidelines for success, constructive cost model–COCOMO, pp 277–293

  39. Ungan E, Cizmeli N, Demirors O (2014) Comparison of functional size based estimation and story points, based on effort estimation effectiveness in scrum projects. In: Software engineering and advanced applications (SEAA), 2014 40th EUROMICRO conference on, IEEE, pp 77–80

  40. Usman M, Mendes E, Weidt F, Britto R (2014) Effort estimation in agile software development: A systematic literature review. In: Proceedings of the 10th international conference on predictive models in software engineering, ACM, pp 82–91

  41. Usman M, Mendes E, Börstler J (2015) Effort estimation in agile software development: a survey on the state of the practice. In: Proceedings of the 19th international conference on evaluation and assessment in software engineering, ACM, p 12

  42. Wu L (1997) The comparison of the software cost estimating methods. http://www.computing.dcu.ie/~renaat/ca421/LWu1.html

  43. Zia ZK, Tipu SK, Zia SK (2012) An effort estimation model for agile software development. Adv Comput Sci Appl 2(1):314–324

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT University, Vellore, Tamil Nadu, India

    Shashank Mouli Satapathy

  2. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Orissa, India

    Santanu Kumar Rath

Authors
  1. Shashank Mouli Satapathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Santanu Kumar Rath
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shashank Mouli Satapathy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Satapathy, S.M., Rath, S.K. Empirical assessment of machine learning models for agile software development effort estimation using story points. Innovations Syst Softw Eng 13, 191–200 (2017). https://doi.org/10.1007/s11334-017-0288-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11334-017-0288-z

Keywords

Search

Navigation