A split Poisson process model for the occurrence of defects and change requests during user acceptance testing

Abstract

Software developed for a specific customer under contract typically undergoes a period of testing by the customer before acceptance. This is known as user acceptance testing and the process can reveal both defects in the system and requests for changes to the product. This paper uses nonhomogeneous Poisson processes to model a real user acceptance data set from a recently developed system. In particular a split Poisson process is shown to provide an excellent fit to the data. The paper explains how this model can be used to aid the allocation of resources through the accurate prediction of occurrences both during the acceptance testing phase and before this activity begins. Furthermore the paper proposes a decision theoretic solution to help the customer decide how long to allow for this phase. The method allows for the cost of defects occurring during operation and the delayed introduction of the system. It also allows for the difference in cost of implementation of change requests during acceptance testing and during operation.