Constructing automated test oracle for low observable software

The application of machine learning techniques for constructing automated test oracles has been successful in recent years. However, existing machine learning based oracles are characterized by a number of deficiencies when applied to software systems with low observability, such as embedded software, cyber-physical systems, multimedia software programs, and computer games. This paper proposes a new black box approach to construct automated oracles that can be applied to software systems with low observability. The proposed approach employs an Artificial Neural Network algorithm that uses input values and corresponding pass/fail outcomes of the program under test as the training set. To evaluate the performance of the proposed approach, extensive experiments were carried out on several benchmarks. The results manifest the applicability of the proposed approach to software systems with low observability and its higher accuracy than a well-known machine learning based method. This study also assessed the effect of different parameters on the accuracy of the proposed approach. © 2020 Sharif University of Technology. All rights reserved.