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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 8 European countries, this 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 our database.

0 I E E E S O F T WA R E J a n u a r y / F e b r u a r y 2 0 0 0 0 7 4 0 -7 4 5 9 / 0 0 / $ 1 0 . 0 0 © 2 0 0 0 I E E E factors affect software-development productivity in any given environment.

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In this paper we present the results of our effort estimation analysis of a European Space Agency database consisting of 108 software development projects. We develop and evaluate simple empirical effort estimation models that include only those productivity factors found to be significant for these projects and determine if models based on a multicompany database can be successfully used to make effort estimations within a specific company. This was accomplished by developing company specific effort estimation models based on the significant productivity factors of a particular company and by comparing the results with those from general ESA models on a holdout sample of the company. To our knowledge, no other published research has yet developed and analysed software development effort estimation models in this way. Effort predictions made on a holdout sample of the individual company's projects using general models were less accurate than the company specific model. However, it is likely that in the absence of enough resources and data for a company to develop its own model, the application of general models may be more accurate than the use of guessing and intuition.software productivity, software metrics, software project management, software development