JavaScript Instrumentation for Search-Based Software Testing: A Study with RESTful APIs

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JavaScript SBST Heuristics to Enable Effective Fuzzing of NodeJS Web APIs

JavaScript is one of the most popular programming languages. However, its dynamic nature poses several challenges to automated testing techniques. In this paper, we propose an approach and open-source tool support to enable white-box testing of JavaScript applications using Search-Based Software Testing (SBST) techniques. We provide an automated approach to collect search-based heuristics like the common Branch Distance and to enable Testability Transformations. To empirically evaluate our results, we integrated our technique into the EvoMaster test generation tool, and carried out analyses on the automated system testing of RESTful and GraphQL APIs. Experiments on eight Web APIs running on NodeJS show that our technique leads to significantly better results than existing black-box and grey-box testing tools, in terms of code coverage and fault detection.publishedVersio

Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio–Temporal Traffic Flow

Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances in the outlier detection area by finding anomalies in spatio-temporal urban traffic flow. It proposes a new approach by considering the distribution of the flows in a given time interval. The flow distribution probability (FDP) databases are first constructed from the traffic flows by considering both spatial and temporal information. The outlier detection mechanism is then applied to the coming flow distribution probabilities, the inliers are stored to enrich the FDP databases, while the outliers are excluded from the FDP databases. Moreover, a k-nearest neighbor for distance-based outlier detection is investigated and adopted for FDP outlier detection. To validate the proposed framework, real data from Odense traffic flow case are evaluated at ten locations. The results reveal that the proposed framework is able to detect the real distribution of flow outliers. Another experiment has been carried out on Beijing data, the results show that our approach outperforms the baseline algorithms for high-urban traffic flow

Multiple agent possibilistic logic

International audienceThe paper presents a ‘multiple agent’ logic where formulas are pairs of the form (a, A), made of a proposition a and a subset of agents A. The formula (a, A) is intended to mean ‘(at least) all agents in A believe that a is true’. The formal similarity of such formulas with those of possibilistic logic, where propositions are associated with certainty levels, is emphasised. However, the subsets of agents are organised in a Boolean lattice, while certainty levels belong to a totally ordered scale. The semantics of a set of ‘multiple agent’ logic formulas is expressed by a mapping which associates a subset of agents with each interpretation (intuitively, the maximal subset of agents for whom this interpretation is possibly true). Soundness and completeness results are established. Then a joint extension of the multiple agent logic and possibilistic logic is outlined. In this extended logic, propositions are then associated with both sets of agents and certainty levels. A formula then expresses that ‘all agents in set A believe that a is true at least at some level’. The semantics is then given in terms of fuzzy sets of agents that find an interpretation more or less possible. A specific feature of possibilistic logic is that the inconsistency of a knowledge base is a matter of degree. The proposed setting enables us to distinguish between the global consistency of a set of agents and their individual consistency (where both can be a matter of degree). In particular, given a set of multiple agent possibilistic formulas, one can compute the subset of agents that are individually consistent to some degree

Recurrent neural network with density-based clustering for group pattern detection in energy systems

This research explores a new direction in power system technology and develops a new framework for pattern group discovery from large power system data. The efficient combination between the recurrent neural network and the density-based clustering enables to find the group patterns in the power system. The power system data is first collected in multiple time series data and trained by the recurrent neural network to find simple patterns. The simple patterns are then studied, and analyzed with the density-based clustering algorithm to identify the group of patterns. The solution was analyzed in two case studies (pattern discovery and outlier detection) specifically for power systems. The results show the advantages of the proposed framework and a clear superiority compared to state-of-the-art approaches, where the average correlation in group pattern detection is 90% and in group outlier detection more than 80% of both true-positive and true-negative rates.publishedVersio

Recurrent neural network with density-based clustering for group pattern detection in energy systems

This research explores a new direction in power system technology and develops a new framework for pattern group discovery from large power system data. The efficient combination between the recurrent neural network and the density-based clustering enables to find the group patterns in the power system. The power system data is first collected in multiple time series data and trained by the recurrent neural network to find simple patterns. The simple patterns are then studied, and analyzed with the density-based clustering algorithm to identify the group of patterns. The solution was analyzed in two case studies (pattern discovery and outlier detection) specifically for power systems. The results show the advantages of the proposed framework and a clear superiority compared to state-of-the-art approaches, where the average correlation in group pattern detection is 90% and in group outlier detection more than 80% of both true-positive and true-negative rates.publishedVersio

Intelligent blockchain management for distributed knowledge graphs in IoT 5G environments

This article introduces a new problem of distributed knowledge graph, in IoT 5G setting. We developed an end-to-end solution for solving such problem by exploring the blockchain management and intelligent method for producing the better matching of the concepts and relations of the set of knowledge graphs. The concepts and the relations of the knowledge graphs are divided into several components, each of which contains similar concepts and relations. Instead of exploring the whole concepts and the relations of the knowledge graphs, only the representative of these components is compared during the matching process. The framework has outperformed state-of-the-art knowledge graph matching algorithms using different scenarios as input in the experiments. In addition, to confirm the usability of our suggested framework, an in-depth experimental analysis has been done; the results are very promising in both runtime and accuracy.publishedVersio

SS-ITS: secure scalable intelligent transportation systems

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Reinforcement learning multi-agent system for faults diagnosis of mircoservices in industrial settings

This paper develops a new framework called MASAD (Multi-Agents System for Anomaly Detection), a hybrid combination of reinforcement learning, and a multi-agents system to identify abnormal behaviors of microservices in industrial environment settings. A multi-agent system is implemented using reinforcement learning, where each agent learns from the given microservice. Intelligent communication among the different agents is then established to enhance the learning of each agent by considering the experience of the agents of the other microservices of the system. The above setting not only allows to identify local anomalies but global ones from the whole microservices architecture. To show the effectiveness of the framework as proposed, we have gone through a thorough experimental analysis on two microservice architectures (NETFLIX, and LAMP). Results showed that our proposed framework can understand the behavior of microservices, and accurately simulate different interactions in the microservices. Besides, our approach outperforms baseline methods in identifying both local and global outliers.submittedVersio

Deep learning based hashtag recommendation system for multimedia data

This work aims to provide a novel hybrid architecture to suggest appropriate hashtags to a collection of orpheline tweets. The methodology starts with defining the collection of batches used in the convolutional neural network. This methodology is based on frequent pattern extraction methods. The hashtags of the tweets are then learned using the convolution neural network that was applied to the collection of batches of tweets. In addition, a pruning approach should ensure that the learning process proceeds properly by reducing the number of common patterns. Besides, the evolutionary algorithm is involved to extract the optimal parameters of the deep learning model used in the learning process. This is achieved by using a genetic algorithm that learns the hyper-parameters of the deep architecture. The effectiveness of our methodology has been demonstrated in a series of detailed experiments on a set of Twitter archives. From the results of the experiments, it is clear that the proposed method is superior to the baseline methods in terms of efficiency.publishedVersio