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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 de ciencies 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 Arti cial 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 e ect of di erent parameters on the accuracy of the proposed approach.

Over the past few decades, networks have been widely used to model real-world phenomena. Real-world networks exhibit nontrivial topological characteristics and therefore, many network models are proposed in the literature for generating graphs that are similar to real networks. Network models reproduce nontrivial properties such as long-tail degree distributions or high clustering coefficients. In this context, we encounter the problem of selecting the network model that best fits a given real-world network. The need for a model selection method reveals the network classification problem, in which a target-network is classified into one of the candidate network models. In this paper, we propose a novel network classification method which is independent of the network size and employs an alignment-free metric of network comparison. The proposed method is based on supervised machine learning algorithms and utilizes the topological similarities of networks for the classification task. The experiments show that the proposed method outperforms state-of-the-art methods with respect to classification accuracy, time efficiency, and robustness to noise.

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