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Decomposed Versus Holistic Estimates of Effort Required for Software Writing Tasks

Author

Listed:
  • Terry Connolly

    (Department of Management and Policy, University of Arizona, Tucson, Arizona 85721)

  • Doug Dean

    (Center for the Management of Information, College of Business and Public Administration, University of Arizona, Tucson, Arizona 85721)

Abstract

We examine decision analysis' central "decomposition principle" in the context of work-time estimates of software writers. Two experiments examined the abilities of advanced programming students to estimate how long they would take to complete specific software projects. They estimated their own work times both for entire projects and for their constituent subtasks. Estimates showed varying degrees of overoptimism and overpessimism but all were much too tight, with almost half of actual outcomes falling in the 1% tails of estimated distributions. This overtightness was unaffected by task decomposition, question wording, question order, or training in estimation. It was, however, significantly reduced by a procedure aimed at inducing generous upper and lower plausible limits. An underlying model of incomplete search is used to connect these findings to existing themes in cognition and judgment research, as well as to practical application. The findings suggest that the best level of decomposition at which to elicit work-time estimates may depend on task, judge, and elicitation method.

Suggested Citation

  • Terry Connolly & Doug Dean, 1997. "Decomposed Versus Holistic Estimates of Effort Required for Software Writing Tasks," Management Science, INFORMS, vol. 43(7), pages 1029-1045, July.
    • Handle: RePEc:inm:ormnsc:v:43:y:1997:i:7:p:1029-1045
    DOI: 10.1287/mnsc.43.7.1029
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    Citations

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    Cited by:

    1. Markus K. Brunnermeier & Filippos Papakonstantinou & Jonathan A. Parker, 2008. "An Economic Model of the Planning Fallacy," NBER Working Papers 14228, National Bureau of Economic Research, Inc.
    2. Franco, L. Alberto & Hämäläinen, Raimo P. & Rouwette, Etiënne A.J.A. & Leppänen, Ilkka, 2021. "Taking stock of behavioural OR: A review of behavioural studies with an intervention focus," European Journal of Operational Research, Elsevier, vol. 293(2), pages 401-418.
    3. Onkal, Dilek & Yates, J. Frank & Simga-Mugan, Can & Oztin, Sule, 2003. "Professional vs. amateur judgment accuracy: The case of foreign exchange rates," Organizational Behavior and Human Decision Processes, Elsevier, vol. 91(2), pages 169-185, July.
    4. Matej Lorko & Maroš Servátka & Le Zhang, 2021. "Improving the Accuracy of Project Schedules," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1633-1646, June.
    5. repec:cup:judgdm:v:11:y:2016:i:2:p:147-167 is not listed on IDEAS
    6. Buehler, Roger & Messervey, Deanna & Griffin, Dale, 2005. "Collaborative planning and prediction: Does group discussion affect optimistic biases in time estimation?," Organizational Behavior and Human Decision Processes, Elsevier, vol. 97(1), pages 47-63, May.
    7. Gilberto Montibeller & Detlof von Winterfeldt, 2015. "Cognitive and Motivational Biases in Decision and Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1230-1251, July.
    8. Ferretti, Valentina & Montibeller, Gilberto & von Winterfeldt, Detlof, 2023. "Testing the effectiveness of debiasing techniques to reduce overprecision in the elicitation of subjective continuous probability distributions," LSE Research Online Documents on Economics 115333, London School of Economics and Political Science, LSE Library.
    9. Yun Shin Lee & Enno Siemsen, 2017. "Task Decomposition and Newsvendor Decision Making," Management Science, INFORMS, vol. 63(10), pages 3226-3245, October.
    10. Buehler, Roger & Griffin, Dale, 2003. "Planning, personality, and prediction: The role of future focus in optimistic time predictions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 92(1-2), pages 80-90.
    11. Jessica Wiese & Roger Buehler & Dale Griffin, 2016. "Backward planning: Effects of planning direction on predictions of task completion time," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 11(2), pages 147-167, March.
    12. Jong Seok Lee & Mark Keil & Eliezer Shalev, 2019. "Seeing the Trees or the Forest? The Effect of IT Project Managers’ Mental Construal on IT Project Risk Management Activities," Information Systems Research, INFORMS, vol. 30(3), pages 1051-1072, September.
    13. Michał Krawczyk, 2011. "Overconfident for real? Proper scoring for confidence intervals," Working Papers 2011-15, Faculty of Economic Sciences, University of Warsaw.
    14. Ferretti, Valentina & Montibeller, Gilberto & von Winterfeldt, Detlof, 2023. "Testing the effectiveness of debiasing techniques to reduce overprecision in the elicitation of subjective continuous probability distributions," European Journal of Operational Research, Elsevier, vol. 304(2), pages 661-675.
    15. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    16. Buehler, Roger & Peetz, Johanna & Griffin, Dale, 2010. "Finishing on time: When do predictions influence completion times?," Organizational Behavior and Human Decision Processes, Elsevier, vol. 111(1), pages 23-32, January.

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