Project productivity evaluation in early software effort estimation

Abstract

The productivity factor has long been a key driver to estimate effort from Use Case Points (UCP) size measure, especially when historical dataset is absent. But, no one questions: Does productivity still matter when historical data are also available? To facilitate answering this question, the present paper studies the role of productivity from 2 perspectives. First, does learning productivity from historical data lead to better accuracy than using fixed productivity ratios? Second, what is the impact of ignoring productivity when estimating the effort from UCP? Five different models that use productivity factor have been used under different experimental settings and compared with some regression models that use only UCP size metrics. We found that dynamically learning and adjusting productivity from historical data are more efficient than using fixed productivity values. Moreover, using UCP size variables to estimate effort tends to be more accurate than using productivity and UCP variables. We also did not find any significant improvement when using UCP adjustment factors for measuring productivity. Finally, we conclude that the productivity factor is a good driver to generate effort estimate from UCP in the presence and absence of historical datasets. But using UCP size variables alone for predicting effort is more accurate than using productivity.

A model of owner estimate cost for software development project in Indonesia

“…Karner suggested using values of 20 man-hours/UCP, 6 while Schneider and Winters 14 suggested using three values, namely, fair (20 person-hours/UCP), low (28 person-hours/UCP), and very low (36 person-hours/ UCP). In another study, Azzeh and Nassif 15 stated that the use of learning productivity obtained from historical data is more appropriate and efficient than using fixed productivity (static productivity).…”

“…You can also use other ER values as suggested by Azzeh and Nassif, namely, learning productivity ratio, if the previous project dataset is present. 15 The value of effort for the nine projects is presented in Table 14.…”

“…Complex A complex use case has the sum of transactions ≥7 (include alternative steps), using three or more database entities, and implementing involves ≥10 classes. 15 essential for project development. The score is then multiplied by each weight of each factor and sums up the whole.…”

“…Other researchers called ER with the term productivity factor (PF) 10 or project productivity, 13,15 others call it person-hours. 30 The ER value is set equal to 20 man-hours/UCP.…”

“…6 Some other researchers base the value of ER on the complexity of the project by assigning value ER = 20 manhours/UCP to the simple project, 24 man-hours/UCP to the medium project, and 36 man-hours/UCP to the complex project. 14 29 Azzeh and Nassif suggested using the learning productivity ratio obtained from historical data because it was more reasonable and efficient, 15 of course, if the dataset for the previous project existed. In this study, we used ER = 8.2 man-hours/UCP because there was no historical data, and this ER 29 was formulated using a software development dataset in Indonesia.…”