Issue No. 12 - December (2004 vol. 30)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TSE.2004.104
Emilia Mendes , IEEE Computer Society
Productivity measures based on a simple ratio of product size to project effort assume that size can be determined as a single measure. If there are many possible size measures in a data set and no obvious model for aggregating the measures into a single measure, we propose using the expression AdjustedSize/Effort to measure productivity. AdjustedSize is defined as the most appropriate regression-based effort estimation model, where all the size measures selected for inclusion in the estimation model have a regression parameter significantly different from zero (p < 0.05). This productivity measurement method ensures that each project has an expected productivity value of one. Values between zero and one indicate lower than expected productivity, values greater than one indicate higher than expected productivity. We discuss the assumptions underlying this productivity measurement method and present an example of its use for Web application projects. We also explain the relationship between effort prediction models and productivity models.
Software productivity measurement, software cost estimation.
B. Kitchenham and E. Mendes, "Software Productivity Measurement Using Multiple Size Measures," in IEEE Transactions on Software Engineering, vol. 30, no. , pp. 1023-1035, 2004.