ISSN 1000-1239 CN 11-1777/TP



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    Journal of Computer Research and Development    2022, 59 (9): 1867-1868.   DOI: 10.7544/issn1000-1239.qy20220901
    Abstract260)   HTML4)    PDF (213KB)(236)       Save
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    Tracking and Querying over Timeseries Data with Schema Evolution
    Zhao Xin, Wan Yingge, Liu Yingbo
    Journal of Computer Research and Development    2022, 59 (9): 1869-1886.   DOI: 10.7544/issn1000-1239.20220012
    Abstract174)   HTML3)    PDF (2822KB)(202)       Save
    In the context of the Internet of things and big data, vast amount of sensors generate massive time series data on daily basis. The fast iterations of software releases lead to frequent changes to the schema of these time series, which makes the management of schema evolution of time series increasingly prominent. Schema evolution requires the management of each version of data schema, so that there is no information loss during schema modification, and data can be accessed across multiple schema versions. Existing timeseries databases management system have limited support for schema evolution, while schema evolution may occur frequently under this circumstance. State-of-art research and technology for schema evolution mainly focus on relational database, struggling with complicated integrity constraint which is more flexible within timeseries database. This paper compares various databases with regard to schema evolution, provide a formal definition to the time series and its schemas, and analyzes the process of schema evolution. This paper designs a data-centric schema evolution tracing and querying system, discusses the key problems of schema tracking and cross schema version query in detail, and implements and tests it on the timeseries database Apache IoTDB. Finally, the performance of the system is evaluated, and the future research is discussed.
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    An Exploratory Adaptive FSM Test Method of Intelligent Service Terminal
    Nie Yuge, Yin Beibei, Pei Hanyu, Li Li, Xu Lixin
    Journal of Computer Research and Development    2022, 59 (9): 1887-1901.   DOI: 10.7544/issn1000-1239.20220023
    Abstract114)   HTML3)    PDF (1547KB)(103)       Save
    With the advent of the intelligent era, intelligent service terminals like automatic beverage vending machines, automatic subway ticketing machines and ATM machines have played an increasingly important role in our lives. Therefore, it is essential to make a comprehensive and effective test to prevent various possible errors and improve the user experience. In view of the problems such as the workload of testing is huge and difficult to be standardized caused by frequent software version updates, difficult connection between development and testing, and testing while developing, based on the characteristics of intelligent service terminal that they have obvious states and state migrations, we put forward an efficient test scheme which can still be used efficiently in the case of absence of detailed specifications or the rapid software iteration requiring continuous regression testing—exploratory adaptive finite state machine (FSM) testing. Firstly, the state and migration information of the system to be tested are obtained through exploratory testing, and then they are modeled as FSM. According to the model and the executed test cases, the test cases are generated based on the state and state migration coverage, and the test model and corresponding test cases are continuously adjusted adaptively in the testing process. Based on this method, an experimental platform is built by integrating the open source software Graphwalker. Ten different kinds of common intelligent service terminals are selected to evaluate their effectiveness through experiments. The experimental results show that the number of test cases generated by this method is small and the degree of test adequacy is high. It can efficiently find the defects and problems in the intelligent service terminal system.
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    A Networked Software Optimization Mechanism Based on Gradient-Play
    Shu Chang, Li Qingshan, Wang Lu, Wang Ziqi, Ji Yajiang
    Journal of Computer Research and Development    2022, 59 (9): 1902-1913.   DOI: 10.7544/issn1000-1239.20220016
    Abstract108)   HTML4)    PDF (2341KB)(88)       Save
    Networked software is a novel type of system deploying services on different devices and running based on the Internet. In order to improve service efficiency and realize a greater variety of functions, more software developers prefer to build systems in this way. However, the highly distributed characteristic brings obstacles to optimization of this kind of software. This paper is aimed at solving the optimization decision issues of networked software based on game theory. We let each software node exchange information with other nodes connecting to them and adjust their states for better payoffs, to achieve the purpose of improving overall system performance. In this process, we apply a consensus-based method to overcome the communication problems used to exist in the networked software system. With the method, each software node can make optimization decisions via incomplete system information. In addition, we propose an adaptive step size mechanism and a forced coordination mechanism to adjust parameters reasonably. These two mechanisms alleviate the problem of divergence and reduce the difficulty of parameter selection in this kind of methods, after that, an efficient synergy between state optimization and coordination of nodes can be realized. The experiments show that the original method can converge to Nash equilibrium more efficiently with these two mechanisms proposed by us.
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    Deep Learning Based Data Race Detection Approach
    Zhang Yang, Qiao Liu, Dong Chunhao, Gao Hongbin
    Journal of Computer Research and Development    2022, 59 (9): 1914-1928.   DOI: 10.7544/issn1000-1239.20220014
    Abstract221)   HTML2)    PDF (2582KB)(167)       Save
    Existing approaches for deep-learning-based data race detection are suffering from the issues of single feature extraction and low accuracy. To improve the state-of-the-art, a novel approach called DeleRace is proposed to detect data race based on deep learning model. Firstly, DeleRace extracts instruction-level, method-level, and file-level features from a variety of real-world applications based on static analysis tool WALA. All these features are transformed by word vectorization to build the training dataset. Secondly, ConRacer, as an existing data race tool, is employed to identify the real race. Based on this tool, those positive samples in the training dataset is labelled. To further optimize the dataset, DeleRace leverages SMOTE algorithm to distribute both positive samples and negative ones in balance. Finally, CNN-LSTM model is constructed and a classifier is trained to detect data race. In the experimentation, a total of 26 real-world applications is selected from different fields in DaCapo, JGF, IBM Contest and PJBench benchmark suites. The experimental results show that the accuracy of DeleRace is 96.79% which is 4.65% higher than existing deep-learning-based approaches. Furthermore, the performance of DeleRace is compared with that of both dynamic tools (such as Said and RVPredict) and static tools (such as SRD and ConRacer), which demonstrates the effectiveness of DeleRace.
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    Evaluating the Fitness of Model Deviation Detection Approaches on Self-Adaptive Software Systems
    Tong Yanxiang, Qin Yi, Ma Xiaoxing
    Journal of Computer Research and Development    2022, 59 (9): 1929-1946.   DOI: 10.7544/issn1000-1239.20220015
    Abstract71)   HTML3)    PDF (1056KB)(52)       Save
    Model deviations in self-adaptive software systems cause critical reliability issues. For control-based self-adaptive systems, model deviation roots in the drifting of the managed system’s nominal model in uncertain running environments, which causes the invalidation of provided formal guarantees, and may lead to system’s abnormal behavior. Existing model deviation detection approaches often ignore the characteristics of model deviations that emerge in different scenarios. This makes it difficult for users to choose an appropriate approach in a specific application scenario. We provide a framework to describe different detection approaches and propose three metrics to evaluate a detection approach’s fitness with respect to different types of model deviations. The provided framework is composed of four parts, namely system modelling, detection variable estimation, model deviation representation, and model deviation judgement, based on the process of model deviation detection. The proposed metrics, including control-signal-intensity, environmental-input-intensity, and uncertainty-intensity, concern three key factors in the process of model deviation detection, respectively. Using these metrics, a deviation scenario is quantified with a vector and is classified by the quantified values into a characteristic scenario according to control theory. A number of experiments are conducted to study the effectiveness of four mainstream model detection approaches in different scenarios, and their fitness to different characteristic scenarios of model deviations is summarized.
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