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A robust optimization model for software development costs considering time value of money.

  • Published In: Journal of Software: Evolution & Process, 2024, v. 36, n. 7. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Namdar, Mohammadreza; Noorossana, Rassoul 3 of 3

Abstract

Determining "software release time" and "testing stop time" is a significant challenge in software projects, as both greatly affect the software cost and reliability. To overcome the drawbacks of past research, this study presents a novel robust optimization approach considering the interval estimation of input parameters for a software reliability growth model. It aims to detect the optimal "software release time" and "testing stop time" to minimize software development costs in an uncertain environment. Additionally, it considers the time value of money for calculating model costs by considering the interest rate and inflation. Generally, this research is the first attempt to use a robust approach for optimizing the software development cost considering the time value of money. The paper investigates the model efficiency in practical situations through a case study and analyzes the effect of the discounted rate and parameters uncertainty on the development cost using a software reliability growth model. The results confirm the prominent role of uncertain parameters and the discounted rate value on software development cost. They also indicate that the proposed mathematical model is more consistent with the actual situation and flexible than the past models with deterministic parameters. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Software: Evolution & Process. 2024/07, Vol. 36, Issue 7, p1
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:2047-7473
  • DOI:10.1002/smr.2632
  • Accession Number:178442480
  • Copyright Statement:Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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