skip to main content
10.1145/1858996.1859049acmconferencesArticle/Chapter ViewAbstractPublication PagesaseConference Proceedingsconference-collections
research-article

Timesheet assistant: mining and reporting developer effort

Published:20 September 2010Publication History

ABSTRACT

Timesheets are an important instrument used to track time spent by team members in a software project on the tasks assigned to them. In a typical project, developers fill timesheets manually on a periodic basis. This is often tedious, time consuming and error prone. Over or under reporting of time spent on tasks causes errors in billing development costs to customers and wrong estimation baselines for future work, which can have serious business consequences. In order to assist developers in filling their timesheets accurately, we present a tool called Timesheet Assistant (TA) that non-intrusively mines developer activities and uses statistical analysis on historical data to estimate the actual effort the developer may have spent on individual assigned tasks. TA further helps the developer or project manager by presenting the details of the activities along with effort data so that the effort may be seen in the context of the actual work performed. We report on an empirical study of TA in a software maintenance project at IBM that provides preliminary validation of its feasibility and usefulness. Some of the limitations of the TA approach and possible ways to address those are also discussed.

References

  1. }}Actitime,. http://www.actitime.com.Google ScholarGoogle Scholar
  2. }}Baralga,. http://baralga.origo.ethz.ch.Google ScholarGoogle Scholar
  3. }}FreeTime,. http://www.zoo2.com.au.Google ScholarGoogle Scholar
  4. }}GNU Octave,. http://www.gnu.org/software/octave/.Google ScholarGoogle Scholar
  5. }}oDesk,. http://www.odesk.com.Google ScholarGoogle Scholar
  6. }}PROxy Based Estimation (PROBE) for Structured Query Language (SQL),. http://www.sei.cmu.edu/reports/06tn017.pdf.Google ScholarGoogle Scholar
  7. }}QSM,. http://www.qsm.com/tools/slim-estimate/index.html.Google ScholarGoogle Scholar
  8. }}Tasktop,. http://www.tasktop.com.Google ScholarGoogle Scholar
  9. }}B. W. Boehm. Software Cost Estimation with COCOMO II. Prentice-Hall, Inc., 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. }}L. Breiman, J. Friedman, C. J. Stone, and R. Olshen. Classification and Regression Trees. Chapman and Hall/CRC, 1 edition, 1984.Google ScholarGoogle Scholar
  11. }}G. Canfora, L. Cerulo, and M. D. Penta. Tracking your changes: A language-independent approach. IEEE Software, 26(1):50--57, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. }}S. Chulani. Bayesian analysis of software cost and quality models. In ICSM, pages 565-, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. }}T. L. Graves and A. Mockus. Inferring change effort from configuration management databases. In IEEE METRICS, pages 267-, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. }}H. H. Kagdi, M. L. Collard, and J. I. Maletic. Towards a taxonomy of approaches for mining of source code repositories. In MSR, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. }}A. Mockus and J. D. Herbsleb. Expertise browser: a quantitative approach to identifying expertise. In ICSE, pages 503--512, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. }}B. Rotibi. Development Intelligence: Business Intelligence for Software-Development. http://www.borland.com/resources/en/pdf/solutions/lqm-ovum-developmental-intellligence.pdf.Google ScholarGoogle Scholar
  17. }}C. K. Roy, J. R. Cordy, and R. Koschke. Comparison and evaluation of code clone detection techniques and tools: A qualitative approach. Sci. Comput. Program., 74(7):470--495, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. }}Y.-S. Seo, K.-A. Yoon, and D.-H. Bae. An empirical analysis of software effort estimation with outlier elimination. In PROMISE '08, pages 25--32, New York, NY, USA, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. }}T. R. L. B. C. Taylor. Qualitative Communication Research Methods. SAGE, Inc., 2002.Google ScholarGoogle Scholar
  20. }}A. Trendowicz, J. Heidrich, J. Münch, Y. Ishigai, K. Yokoyama, and N. Kikuchi. Development of a hybrid cost estimation model in an iterative manner. In ICSE, pages 331--340, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. }}C. Weiß, R. Premraj, T. Zimmermann, and A. Zeller. How long will it take to fix this bug? In MSR, page 1, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. }}T. Zimmermann. Fine-grained processing of cvs archives with apfel. In ETX, pages 16--20, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. }}T. Zimmermann, S. Kim, A. Zeller, and E. J. W. Jr. Mining version archives for co-changed lines. In MSR, pages 72--75, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Timesheet assistant: mining and reporting developer effort

      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 Conferences
        ASE '10: Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering
        September 2010
        534 pages
        ISBN:9781450301169
        DOI:10.1145/1858996

        Copyright © 2010 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: 20 September 2010

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate82of337submissions,24%

        Upcoming Conference

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader