A Prioritization Method for SPL Pairwise Testing Based on User Profiles

Abstract

In Software Product Line (SPL) development, one of promising techniques for core asset testing is to test a subset of SPL as representative products. SPL pairwise testing is a such technique in which each product corresponds to a possible feature configuration in the feature model (FM) and representative products are selected so as to all possible feature pairs are included. It is also important to prioritize representative products, because it could improve the effectiveness of core asset testing especially when the testing resource is limited. In this paper, we propose a prioritization method for SPL pairwise testing based on user profiles. A user profile is a set of user groups and their occurrence probabilities such as the percentages of user groups in a market that use specific devices, applications or services. These profiles are used as the probabilities of feature choices at decision points such as optional features and alternative features in a FM. Based on that, we calculate the probability for obtaining a feature pairs (PFP for short), and generate representative products with priority. Most researches relate to the probabilities about FM handle the probability for obtaining a single feature (PSF for short). Based on PSF, we could estimate PFP. However, this estimation is not appropriate for the prioritization especially when conditional probabilities appear in user profiles. In our method, we directly calculate PFP and determine the priorities. We evaluate the method to show advantages of prioritizations using PFP over those using PSF, and also analyze the characteristics of the method.