Improving Software Development Effort Estimation with a Novel Design Pattern Model

Chidambaram Subbiah (), Andrea C. Hupman (), Haitao Li () and Joseph Simonis ()
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Chidambaram Subbiah: Edward Jones, St. Louis, Missouri 63131
Andrea C. Hupman: Supply Chain and Analytics Department, University of Missouri-St. Louis, St. Louis, Missouri 63121
Haitao Li: Supply Chain and Analytics Department, University of Missouri-St. Louis, St. Louis, Missouri 63121
Joseph Simonis: Edward Jones, St. Louis, Missouri 63131

Interfaces, 2023, vol. 53, issue 3, 192-206

Abstract: A U.S. Midwestern Fortune 500 financial services firm develops software capabilities in-house and requires predictions of project needs for efficient resource allocation decisions across the many projects operating simultaneously. The company develops a novel prediction tool based on the projects’ required software development tasks as described by firm-specific design patterns. The firm provides these predictions within a set of estimates based on industry standard function count methods as well as firm-specific predictive models based on function points and on initial labor assignments. Company management is thus equipped with predictions from multiple methodologies and multiple information sources, enhancing the firm’s ability to predict project needs. Managers aggregate the forecasts, with prediction performance estimated to improve by 35%–49%, measured relative to estimates of the absolute percentage error of the prior method. The improved predictions provide a significant advantage to planning decisions and efficient internal operations. Insights to how managers aggregate the set of forecasts and insights to how the models contribute to the scaled value of information are discussed and further illustrate the benefits of the approach.

Keywords: forecasting; software estimation; value of information; decision analysis (search for similar items in EconPapers)
Date: 2023
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