State of the practice: An exploratory analysis of schedule estimation and software project success prediction

  • Authors:

  • J. M. Verner;W. M. Evanco;N. Cerpa

  • Affiliations:

  • National ICT Australia, Australian Technology Park, Alexandria, Sydney, NSW 1430, Australia;College of Information Science and Technology, Drexel University Philadelphia, PA 19104, USA;Department of Systems Engineering, Faculty of Engineering University of Talca, Talca, Chile

  • Venue:
  • Information and Software Technology
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

During discussions with a group of U.S. software developers we explored the effect of schedule estimation practices and their implications for software project success. Our objective is not only to explore the direct effects of cost and schedule estimation on the perceived success or failure of a software development project, but also to quantitatively examine a host of factors surrounding the estimation issue that may impinge on project outcomes. We later asked our initial group of practitioners to respond to a questionnaire that covered some important cost and schedule estimation topics. Then, in order to determine if the results are generalizable, two other groups from the US and Australia, completed the questionnaire. Based on these convenience samples, we conducted exploratory statistical analyses to identify determinants of project success and used logistic regression to predict project success for the entire sample, as well as for each of the groups separately. From the developer point of view, our overall results suggest that success is more likely if the project manager is involved in schedule negotiations, adequate requirements information is available when the estimates are made, initial effort estimates are good, take staff leave into account, and staff are not added late to meet an aggressive schedule. For these organizations we found that developer input to the estimates did not improve the chances of project success or improve the estimates. We then used the logistic regression results from each single group to predict project success for the other two remaining groups combined. The results show that there is a reasonable degree of generalizability among the different groups.