In this special issue, five representative papers focusing on the aforementioned directions are chosen.
In the paper "Approach to Generating TAP Rules in IoT System Based on Environmental Modeling", a TAP rule generation approach based on environmental modeling is proposed for managing and controlling IoT devices of smart buildings and smart homes, which automatically derives system behavior from service requirements based on the environmental model, detects the integrity and consistency of the system behavior, and finally generates TAP rules.
In the paper "Efficient Blockchain-Empowered Data Sharing Incentive Scheme for Internet of Things", the method based on blockchain incentive mechanism for data-sharing and improving sharing efficiency in IoT systems is investigated. A sharding technology is employed to build asynchronous consensus zones that are capable of processing data-sharing transactions in parallel, and efficient consensus mechanisms on the cloud/edge servers and the sharded asynchronous consensus zones are deployed to improve the processing efficiency of data-sharing transactions.
In the paper "A Meta-Modeling Approach for Autonomous Driving Scenario Based on STTD", a Spatio-Temporal Trajectory Data (STTD) meta-modeling method oriented to autonomous driving scenarios is proposed to realize the unification, processing, and reuse of data. The use of the Adaptive Domain-Specific Modeling Language (ADSML) to instantiate a scene is then discussed.
In the paper "Deep Learning-Based Hybrid Fuzz Testing", a deep learning-based hybrid testing method that combines symbolic execution and fuzzing is proposed considering the respective advantages and disadvantages of symbolic execution and fuzzing methods. The corresponding hybrid testing tool, SmartFuSE, is then designed.
In the paper "Structurally-Enhanced Approach for Automatic Code Change Transformation", automatic conversion of similar codes in the course of code changes is studied. A deep learning-based, structurally-enhanced approach for automatic code change transformation method is proposed. This method enhances the model's ability to capture the structure information and dependency information of the code, thereby improving the accuracy of automatic transformation of code changes.
This special issue is oriented to the researchers and engineers in domain software, with the contents covering various fields of domain software, such as requirement analysis, design and modeling, development and construction, testing and verification, reflecting the high-level research achievements of Chinese researchers in related fields. We hope this special issue can provide insights to the studies of domain software.