By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT applications. Most of the current research studies evaluated the fault prediction methods using simulation environments. However, formal verification of the correctness of a fault prediction method has not been reported yet. This paper presents a behavioral modeling and formal verification of a hybrid machine learning-based fault prediction model with Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) algorithms. In particular, the PSO is used for feature selection. Then, the fault prediction is considered as a behavior to be verified formally. The fault prediction behavior is divided into two types of behaviors: dimension reduction behavior and prediction behavior. For each of the behaviors, one formal model is designed. The behavioral models designed are mapped into the Labeled Transition System (LTS). The Process Analysis Toolkit (PAT) model checker is employed to evaluate the behavioral models. The accuracy of the fault prediction method is done by some existing specifications such as deadlock-free and reachability properties in terms of linear temporal logic formulas. Also, the verification of the fault prediction behaviors is used to detect the defect metrics of information-centric IoT applications. Experimental results showed that our proposed verification method has minimum verification time and memory usage for evaluating critical specification rules than other research studies. INDEX TERMS Internet of Things applications, fault prediction, formal verification, process analysis toolkit, multi-layer perceptron, particle swarm optimization.
Combinatorial optimization has been used in different research areas. It has been employed successfully in software testing fields to construct minimum set of combinations (i.e., in terms of size) which in turn represents the minimum number of test cases. It was also found to be a successful approach that can be applied to solve other similar problems in different fields of research. In line with this approach, this paper presents a new application of the combinational optimization in the design of PID controller for DC servomotor. The design of PID controller involves the determination of three parameters. To find optimal initial PID parameters, different tuning methods have been proposed and designed in the literature. The combinatorial design is concerned with the arrangement of finite set of elements into combinatorial set that satisfies some given constraints. Consequently, the proposed method takes the interaction of the input parameters as a constraint for constructing this combinatorial set. The generated sets are then used in the proposed tuning method. The method proved its effectiveness within a set of experiments in a simulated environment.
Author gender detection (AGD) is a serious and crucial issue in Internet security applications, in particular in email, messenger, and social network communications. Detecting the gender of communication partner helps preventing massive fraud and abuses happening through social media such as email, blogs, forums. Text and writings of people on the Internet have valuable information that can be used to identify the gender of an author. Machine learning and meta-heuristic algorithms are valuable techniques to extract hidden patterns useful for detecting gender of a text. In this paper, an artificial neural network (ANN) is employed as a classifier to detect the gender of an email author and the whale optimization algorithm (WOA) is used to find optimal weights and biases for improving the accuracy of the ANN classification. Through this combination of ANN and WOA an accuracy of 98%, precision of 97.16%, and recall of 99.67% were achieved, which indicates the superiority of the proposed method on Bayesian networks, regression, decision tree, support vector machine, and ANN examined. INDEX TERMS Author gender detection, machine learning, artificial neural network, whale optimization algorithm.
To ensure the quality of current highly configurable software systems, intensive testing is needed to test all the configuration combinations and detect all the possible faults. This task becomes more challenging for most modern software systems when constraints are given for the configurations. Here, intensive testing is almost impossible, especially considering the additional computation required to resolve the constraints during the test generation process. In addition, this testing process is exhaustive and time-consuming. Combinatorial interaction strategies can systematically reduce the number of test cases to construct a minimal test suite without affecting the effectiveness of the tests. This paper presents a new efficient searchbased strategy to generate constrained interaction test suites to cover all possible combinations. The paper also shows a new application of constrained interaction testing in software fault searches. The proposed strategy initially generates the set of all possible t − tuple combinations; then, it filters out the set by removing the forbidden t − tuples using the base forbidden tuple (BFT) approach. The strategy also utilizes a mixed neighborhood tabu search (TS) to construct optimal or near-optimal constrained test suites. The efficiency of the proposed method is evaluated through a comparison against two well-known state-of-the-art tools. The evaluation consists of three sets of experiments for 35 standard benchmarks. Additionally, the effectiveness and quality of the results are assessed using a real-world case study. Experimental results show that the proposed strategy outperforms one of the competitive strategies, ACTS, for approximately 83% of the benchmarks and achieves similar results to CASA for 65% of the benchmarks when the interaction strength is 2. For an interaction strength of 3, the proposed method outperforms other competitive strategies for approximately 60% and 42% of the benchmarks. The proposed strategy can also generate constrained interaction test suites for an interaction strength of 4, which is not possible for many strategies. The real-world case study shows that the generated test suites can effectively detect injected faults using mutation testing.Software testing plays a significant role in specifying the quality of developed software. High-quality software can be achieved by employing an intensive testing process to detect most of the bugs. Ideally, identifying all possible bugs is achievable by applying exhaustive testing with a large test suite. However, exhaustive testing leads to more time spent in software testing, which increases the development cost. Software that is released without being tested well (due to a release deadline constraint or any other reasons) loses trust in the market. As estimated in [1], software faults can generate a drop in the product stock price of 4 − 6% on average (for companies encountering multiple software failures) and further generate almost 3 billion dollars of market losses. Hence, software testers should gener...
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