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Improving Software Development Effort Estimation with a Novel Design Pattern Model

Subbiah, Chidambaram ; Hupman, Andrea C. ; Li, Haitao ; Simonis, Joseph

INFORMS journal on applied analytics, 2023-05, Vol.53 (3), p.192-206 [Periódico revisado por pares]

Linthicum: Institute for Operations Research and the Management Sciences

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  • Título:
    Improving Software Development Effort Estimation with a Novel Design Pattern Model
  • Autor: Subbiah, Chidambaram ; Hupman, Andrea C. ; Li, Haitao ; Simonis, Joseph
  • Assuntos: Capabilities ; Computer programming ; Computer software industry ; Decisions ; Estimates ; Financial services ; Financial services industry ; Function points ; Industry standards ; Information sources ; Managers ; Mathematical models ; Methods ; Prediction models ; Resource allocation ; Software ; Software development
  • É parte de: INFORMS journal on applied analytics, 2023-05, Vol.53 (3), p.192-206
  • Descrição: 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. History: This paper was refereed. This paper was accepted for the Special Issue of INFORMS Journal on Applied Analytics—Decision Analysis.
  • Editor: Linthicum: Institute for Operations Research and the Management Sciences
  • Idioma: Inglês
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