skip to main content
10.1145/2745802.2745813acmotherconferencesArticle/Chapter ViewAbstractPublication PageseaseConference Proceedingsconference-collections
research-article

Effort estimation in agile software development: a survey on the state of the practice

Published:27 April 2015Publication History

ABSTRACT

Context: There are numerous studies on effort estimation in Agile Software Development (ASD) and the state of the art in this area has been recently documented in a Systematic Literature Review (SLR). However, to date there are no studies on the state of the practice in this area, focusing on similar issues to those investigated in the above-mentioned SLR. Objectives: The aim of this paper is to report on the state of the practice on effort estimation in ASD, focusing on a wide range of aspects such as the estimation techniques and effort predictors used, to name a few. Method: A survey was carried out using as instrument an on-line questionnaire answered by agile practitioners who have experience in effort estimation. Results: Data was collected from 60 agile practitioners from 16 different countries, and the main findings are: 1) Planning poker (63%), analogy (47%) and expert judgment (38%) are frequently practiced estimation techniques in ASD; 2) Story points is the most frequently (62%) employed size metric, used solo or in combination with other metrics (e.g., function points); 3) Team's expertise level and prior experience are most commonly used cost drivers; 4) 52% of the respondents believe that their effort estimates on average are under/over estimated by an error of 25% or more; 5) Most agile teams take into account implementation and testing activities during effort estimation; and 6) Estimation is mostly performed at sprint and release planning levels in ASD. Conclusions: Estimation techniques that rely on experts' subjective assessment are the ones used the most in ASD, with effort underestimation being the dominant trend. Further, the use of multiple techniques in combination and story points seem to present a positive association with estimation accuracy, and team-related cost drivers are the ones used by most agile teams. Finally, requirements and management related issues are perceived as the main reasons for inaccurate estimates.

References

  1. D. Azhar, P. Riddle, E. Mendes, N. Mittas, and L. Angelis. Using ensembles for web effort estimation. In Empirical Software Engineering and Measurement, 2013 ACM/IEEE International Symposium on, pages 173--182. IEEE, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  2. F. O. Bjørnson and T. Dingsøyr. Knowledge management in software engineering: A systematic review of studied concepts, findings and research methods used. Information and Software Technology, 50(11): 1055--1068, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. L. Cao. Estimating agile software project effort: an empirical study. 2008.Google ScholarGoogle Scholar
  4. M. Cohn. Agile estimating and planning. Pearson Education, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. A. Fink. The survey handbook, volume 1. Sage, 2003.Google ScholarGoogle Scholar
  6. D. Gotterbarn, K. Miller, and S. Rogerson. Computer society and acm approve software engineering code of ethics. Computer, 32(10): 84--88, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  7. S. Grimstad, M. Jorgensen, and K. Molokken-Ostvold. The clients' impact on effort estimation accuracy in software development projects. In Software Metrics, 2005. 11th IEEE International Symposium, pages 10--pp. IEEE, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Jorgensen and K. Molokken-Ostvold. Reasons for software effort estimation error: impact of respondent role, information collection approach, and data analysis method. Software Engineering, IEEE Transactions on, 30(12): 993--1007, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S. Keaveney and K. Conboy. Cost estimation in agile development projects. 2006.Google ScholarGoogle Scholar
  10. S. Keele. Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, EBSE Technical Report EBSE-2007-01, 2007.Google ScholarGoogle Scholar
  11. B. Kitchenham and S. L. Pfleeger. Principles of survey research parts 1--6. ACM SIGSOFT Software Engineering Notes, Nov 2001 to Mar 2003.Google ScholarGoogle Scholar
  12. O. Liskin, R. Pham, S. Kiesling, and K. Schneider. Why we need a granularity concept for user stories. In Agile Processes in Software Engineering and Extreme Programming, pages 110--125. Springer, 2014.Google ScholarGoogle Scholar
  13. V. Mahnič and T. Hovelja. On using planning poker for estimating user stories. Journal of Systems and Software, 85(9): 2086--2095, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. K. Molokken-Ostvold and K. M. Furulund. The relationship between customer collaboration and software project overruns. In Agile Conference (AGILE), 2007, pages 72--83. IEEE, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. K. Molokken-Ostvold and M. Jorgensen. A comparison of software project overruns-flexible versus sequential development models. Software Engineering, IEEE Transactions on, 31(9): 754--766, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. K. Moløkken-Østvold, M. Jørgensen, S. S. Tanilkan, H. Gallis, A. C. Lien, and S. Hove. A survey on software estimation in the norwegian industry. In Software Metrics, 2004. Proceedings. 10th International Symposium on, pages 208--219. IEEE, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. T. Punter, M. Ciolkowski, B. Freimut, and I. John. Conducting on-line surveys in software engineering. In Empirical Software Engineering, 2003. ISESE 2003. Proceedings. 2003 International Symposium on, pages 80--88. IEEE, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. C. Robson. Real world research: a resource for users of social research methods in applied settings. Wiley Chichester, 2011.Google ScholarGoogle Scholar
  19. M. Usman, E. Mendes, F. Weidt, and R. Britto. Effort estimation in agile software development: A systematic literature review. In Proceedings of the 10th International Conference on Predictive Models in Software Engineering, PROMISE '14, pages 82--91, New York, NY, USA, 2014. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. K. E. Wiegers. More about software requirements. Microsoft Press, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. C. Wohlin, P. Runeson, M. Höst, M. C. Ohlsson, B. Regnell, and A. Wesslén. Experimentation in software engineering. Springer, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Effort estimation in agile software development: a survey on the state of the practice

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        EASE '15: Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering
        April 2015
        305 pages
        ISBN:9781450333504
        DOI:10.1145/2745802

        Copyright © 2015 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 27 April 2015

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        EASE '15 Paper Acceptance Rate20of65submissions,31%Overall Acceptance Rate71of232submissions,31%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader