An empirical model of enhancement-induced defect activity in software

This study exploits the relationship between functional enhancement (FE) activity and defect distribution to produce a model for predicting FE induced defect activity. We achieve this in 2 steps: (1) apply canonical correlation analysis to model the relationship between a set of FE activity indicators and a set of defect activity indicators; this analysis isolates 1 dimension of this relationship having strong correlation; and (2) model the relationship between the latent variables at this dimension as a simple linear regression; this model demonstrates predictive quality sufficient for application as a software engineering tool. The predictive model considers FE activity as the sole source of variation in defect activity. Other sources of variation are not modeled, but remained constant throughout the development effort that yielded the modeled data. Models developed with this technique are intended for predicting defect activity in the program modules that result from the next iteration of the same development process, in production of the next release of the modeled product, with the same key people implementing the software changes that introduce FE. Even in this application, software engineers should understand and control the impacts of the unmodeled sources of variation. The modeling technique scales to larger development efforts involving several key people by either developing unique models for each area of responsibility, or adding independent variables that account for variation introduced by differing skill and understanding levels.