A replicated assessment and comparison of common software cost modeling techniques

  • Authors:

  • Lionel C. Briand;Tristen Langley;Isabella Wieczorek

  • Affiliations:

  • Carleton University Systems and Computer Engineering Department. Ottawa, ON K1S 5B6 Canada;CAESAR, University of New South Wales Kensington, NSW 2052 Sydney, Australia;Fraunhofer Institute for Experimental Software Engineering, Sauerwiesen 6, 67661 Kaiserslautern, Germany

  • Venue:
  • Proceedings of the 22nd international conference on Software engineering
  • Year:
  • 2000

Quantified Score

Hi-index 0.00

Visualization

Abstract

Delivering a software product on time, within budget, and to an agreed level of quality is a critical concern for many software organizations. Underestimating software costs can have detrimental effects on the quality of the delivered software and thus on a company's business reputation and competitiveness. On the other hand, overestimation of software cost can result in missed opportunities to funds in other projects. In response to industry demand, a myriad of estimation techniques has been proposed during the last three decades. In order to assess the suitability of a technique from a diverse selection, its performance and relative merits must be compared.The current study replicates a comprehensive comparison of common estimation techniques within different organizational contexts, using data from the European Space Agency. Our study is motivated by the challenge to assess the feasibility of using multi-organization data to build cost models and the benefits gained from company-specific data collection. Using the European Space Agency data set, we investigated a yet unexplored application domain, including military and space projects. The results showed that traditional techniques, namely, ordinary least-squares regression and analysis of variance outperformed Analogy-based estimation and regression trees. Consistent with the results of the replicated study no significant difference was found in accuracy between estimates derived from company-specific data and estimates derived from multi-organizational data.