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With the rapid increase of software scale and complexity, the cost of traditional software testing methods will increase faster than the scale of software. In order to improve test efficiency, it is particularly important to automatically generate high-quality test cases. This paper introduces a framework for automatic test data generation based on the generative adversarial network (GAN). GAN is employed to train a generative model over execution path information to learn the behavior of the software. Then we can use the trained generative model to produce new test data, and select the test data that can improve the branch coverage according to our proposed selection strategy. Compared to prior work, our proposed method is able to handle programs under test with large-scale branches without analyzing branch expressions. In the experiment, we exhibit the performance of our method by using two modules in GNU Scientific Library. In particular, we consider the application of our method in two testing scenarios; unit testing and integration testing, and conduct a series of experiments to compare the performance of three types of GAN models. Results indicate that the WGAN-GP shows the best performance in our framework. Compared with the random testing method, the WGAN-GP based framework improves the test coverage of five functions out of the seven in the unit testing.

Utilization data are defined as the time series data consisting of time fractions of busy periods in fixed time intervals and are practically used to represent server conditions, such as CPU utilization. In general, it is more challenging to estimate the model parameters from the utilization data since we do not know the exact job arrival time and the service time from the utilization data. In this paper, we consider an approach to estimate the model parameters from the utilization data by assuming a few model assumptions. In particular, we suppose an Mt/M/1/K queueing system whose job arrival follows a Non-homogeneous Poisson Process (NHPP) and propose a parameter estimation method for the NHPP approximately from the utilization data based on the maximum likelihood estimation (MLE) via the expectation maximization (EM) algorithm. In numerical experiments, we generate the simulated utilization data of an Mt/M/1/K queueing system and investigate the effectiveness of our method. Also, we use the real CPU utilization data to exhibit the performance evaluation.

Mobile cloud computing (MCC) is a state-of-the-art architecture that integrates the cloud computing into the mobile environment and overcomes obstacles, such as processing capability, battery life, storage, and availability. Also the MCC is expected to be a key technology for cyber physical systems by connecting to vehicular systems, medical systems, and other mission-critical systems. Therefore, it is a critical issue for MCC to guarantee the high reliability. In this paper, we consider the component importance analysis of an MCC with common-cause failures (CCFs) by using a Markov reward modelbased componentwise sensitivity approach. The component importance analysis is capable of quantifying the criticality of components and helps us to design the highly reliable system. In particular, this paper examines the effect of CCFs on the MCC. Our experimental results show that the preferred action to improve the availability of system with CCFs efficiently is to decrease the failure rate of the cloud node in the cloud infrastructure.

Dynamic power management (DPM) plays a significant role to save power consumption effectively in both the design and operational phases of computer-based systems. It is well known that the state-dependent control policy by monitoring energy states in each component or the whole system is efficient for power saving in server systems whose system state, such as transaction request, can be completely observed. In this paper, we consider an optimal power-aware design in a cluster system and formulate the DPM problem by means of the Markov decision process. We derive the dynamic programming equation for the optimal control policy, which maximizes the expected reward per unit electrical power, which is called the power effectiveness, and give the policy iteration algorithm to determine the optimal control policy sequentially. In numerical experiments, we show the optimal control policy for an example of a cluster system with two service nodes, where the arrival stream of the transaction request is described as a Markov modulated Poisson process. In addition, based on the access data of an enterprise system, the optimal power-aware control for the cluster system and its effectiveness is examined.