A Bayesian Approach for Assessing Software Quality and Productivity
Watts S. Humphrey, Nozer D. Singpurwalla · International Journal of Reliability Quality and Safety Engineering · 1998
This paper introduces a statistical model for analyzing and assessing human-performance parameters such as software quality and productivity. We may refer to this model as a growth curve model because, for suitably chosen values of its constants, we can fit a learning curve to data with trend characteristics. Using data from a programming professional's experience with a range of exercises, we demonstrate the use of this model for assessing software quality and productivity. The model uses a nonhomogeneous autoregressive process, in which Bayesian statistical procedures have been developed. To obtain the desired capability, the model uses more complex statistical methods and requires more extensive initial condition estimates than methods based on exponential smoothing. As a consequence, this model should generally provide superior facilities for tracking and measuring overall trends. Since this is a trend-following model, its projections will tend to excel for highly trended data. On the other hand, since each new value is projected by applying the current trend information to the most recent data, its projections will not be very accurate in highly variable situations. The methodology of this paper is general and can be applied to situations other than those of software quality and productivity.