A variability-based testing approach for synthesizing video sequences

Abstract

A key problem when developing video processing software is the difficulty to test different input combinations. In this paper, we present VANE, a variability-based testing approach to derive video sequence variants. The ideas of VANE are i) to encode in a variability model what can vary within a video sequence; ii) to exploit the variability model to generate testable configurations; iii) to synthesize variants of video sequences corresponding to configurations. VANE computes T-wise covering sets while optimizing a function over attributes. Also, we present a preliminary validation of the scalability and practicality of VANE in the context of an industrial project involving the test of video processing algorithms.