This paper investigates two essential data-driven, software cost modeling: questions related to (1) What modeling _ _ techniques are likely to yield more accurate results when using typical software development cost data?and (2) What are the benefits and drawbacks of using organizationspecific data as compared to multi-organization databases?The former question is important in guiding software cost analysts in their choice of the right type of modeling technique, if at all possible.In order to address this issue, we assess and compare a selection of common cost modeling techniques fulfilling a number of important criteria using a large multi-organizational database in the business application domain.Namely, these are: ordinary least squares regression, stepwise ANOVA, CART, and analogy.The latter question is important in order to assess the feasibility of using multi-organization cost databases to build cost models and the benefits gained from local, company-specific data collection and modeling.As a large subset of the data in the multi-company database came from one organization, we were able to investigate this issue by comparing organization-specific models with models based on multi-organization data.Results show that the performances of the modeling techniques considered were not significantly different, with the exception of the analogy-based models which appear to be less accurate.Surprisingly, when using standard cost factors (e.g., COCOMO-like factors, Function Points), organization specific models did not yield better results than generic, multi-organization models.Permission to make digital or hard topics c~fall or part of this work fbl personal or classroom USC is granted without fee provided that topics are not made or distributed for profit or commercial advnntage and that copies hear this notice and the full citation cm the tkt page.To CojJy othcrwiso, to republish, to post on servers or to redistribute to lists.requires prior specific permission and/or a fee.

The identification, combination, and interaction of the many factors which influence software development productivity makes the measurement, estimation, comparison and tracking of productivity rates very difficult. Through the analysis of a European Space Agency database consisting of 99 software development projects from 37 companies in a European countries, the paper seeks to provide significant and useful Information about the major factors which influence the productivity of European space, military, and industrial applications, as well as to determine the best metric for measuring the productivity of these projects. Several key findings emerge from the study. The results indicate that some organizations are obtaining significantly higher productivity than others. Some of this variation is due to the differences in the application category and programming language of projects in each company; however, some differences must also be due to the ways in which these companies manage their software development projects. The use of tools and modern programming practices were found to be major controllable factors in productivity improvement. Finally, the lines-of-code productivity metric is shown to be superior to the process productivity metric for projects in the authors' database.