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With the widespread popularity of Android devices, the number of Android applications has increased dramatically in recent years. In order to assure the quality of the applications, Android testing has drawn extensive attention. This paper proposes an approach to automate the testing of Android applications based on the Capture and Replay method. Particularly, the user events of Android applications are captured and converted into Robotium test scripts that can be executed to replay the recorded actions of users. The approach also allows inserting assertions when capturing user interactions for verifying the outputs of Android UI components. A supporting tool is implemented to illustrate the usefulness of the proposed approach.

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The determination of the critical path (CP) in stochastic networks is difficult. It is partly due to the randomness of path durations and partly due to the probability issue of the selection of the critical path in the network. What we are confronted with is not only the complexity among random variables but also the problem of path dependence of the network. Besides, we found that CP is not necessarily the longest (or shortest) path in the network, which was a conventional assumption in use. The Program Evaluation and Review Technique (PERT) and Critical Path Index (CPI) approaches are not able to deal with this problem efficiently. In this study, we give a new definition on the CP in stochastic network and propose a modified label-correcting tracing algorithm (M-LCTA) to solve it. Based on the numerical results, compared with Monte Carlo simulation (MCS), the proposed approach can accurately determine the CP in stochastic networks.

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Multitudinous parameters involved have made the direct methanol fuel cell (DMFC) acomplex "black-box," posing challenges and difficulties in its modeling. This paper presents a neural network (NN) model with immune-based particle swarm optimization (IPSO) approach of the DMFC system, which is different from the conventional complex mathematical models. With the actual operation of DMFC taken into consideration, the polarization curves are run under a stable condition as the reference data for training the model. To reduce time cost for the training procedure and maintaining minimum modeling error, the IPSO algorithm is applied to the learning procedure of NN model. By combining the NN and the IPSO, the weight of the transfer function on the node in the hidden layer can be adjusted to minimize modeling error. The simulation results were in agreement with the experimental results, showing that the hybridization of NN model with IPSO approach can effectively demonstrate the polarization behaviors on a DMFC system. Therefore, this hybrid NN model with IPSO approach can be used as a simulation tool, which can save much money and time for reforming the conventional mathematical models with expensive experiment. Furthermore, the proposed method reveals an adaptive ability to improve the model even if the DMFC system structure is different.