Abstract: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.

Abstract:User requirements are the fundamental driving force of smart services in Internet of Things (IoT). Today, many IoT frameworks such as IFTTT allow end users to use simple Trigger-Action Programming (TAP) rules for programming. However, these rules describe device scheduling instructions instead of user service requirements. Some IoT systems propose goal-oriented requirement approaches to support service goal decomposition. Nevertheless, it is difficult to ensure the consistency of different services and completeness of service deployment. To achieve correct "user programming" in IoT systems and ensure the consistency and completeness of user service requirements, this study proposes an environment modeling-based approach to automatically generate TAP rules. On the basis of the service requirements provided by users, required system behaviors are automatically extracted according to the environment model. After their consistency and completeness are checked, TAP rules are generated, which realizes automatic generation from user service requirements to device scheduling instructions. The environment ontology of IoT application scenarios is constructed for environment modeling, and the description method of service requirements based on the environment ontology is also defined. Finally, the accuracy, efficiency, performance of the approach, and the time cost for building the environment ontology are evaluated with a smart home scenario. The results show that the accuracy, efficiency, and performance of this approach exceed the available threshold, and the time cost in building the environment ontology can be ignored when the number of requirements reaches a certain level.

Abstract:In recent years, with many devices continuously joining the Internet of Things (IoT), data sharing as the main driver of the IoT market has become a research hotspot. However, the users are reluctant to participate in data sharing due to security concerns and lacking incentive mechanisms in the current IoT. In this context, blockchain is introduced into the data sharing of IoT to solve the trust problem of users and provide secure data storage. However, in the exploration of building a secure distributed data sharing system based on the blockchain, how to break the inherent performance bottleneck of blockchain is still a major challenge. For this reason, the efficient blockchain-based data sharing incentive scheme is studied for IoT. In the scheme, an efficient data sharing incentive framework based on blockchain is proposed, named ShareBC. Firstly, ShareBC uses sharding technology to build asynchronous consensus zones that can process data sharing transactions in parallel and deploy efficient consensus mechanisms on the cloud/edge servers and asynchronous consensus zones in sharding, thus improving the processing efficiency of data sharing transactions. Then, a sharing incentive mechanism based on a hierarchical data auction model implemented by a smart contract is presentedto encourage IoT users to participate in data sharing. The proposed mechanism can solve the problem of multi-layer data allocation involved in IoT data sharing and maximize the overall social welfare. Finally, the experimental results show that the proposed scheme is economically efficient, incentive-compatible, and real-time, with scalability, low cost, and good practicability.

Abstract:In the current autonomous driving scenario modeling and simulation field, autonomous driving modeling driven by Spatio-Temporal Trajectory Data (STTD) is a key problem, which is significant to improve the safety of the system. In recent years, great progress has been achieved in the modeling and application of STTD, and the application of this data in specific fields has attracted wide attention. However, because STTD has diversity and complexity as well as massive, heterogeneous, dynamic characteristics, the research in the safety-critical field modeling still faces challenges, including unified metadata of spatio-temporal trajectories, meta-modeling methods based on STTD, data processing based on the data analysis of spatio-temporal trajectories, and data quality evaluation. In view of the modeling requirements in the field of autonomous driving, a meta-modeling approach is proposed to construct spatio-temporal trajectory metadata based on Meta Object Facility (MOF) meta-modeling system. According to the characteristics of spatio-temporal trajectory data and autonomous driving domain knowledge, a meta-model of spatio-temporal trajectory data is constructed. Then, we study the modeling approach of autonomous driving safety-critical scenarios based on the spatio-temporal trajectory data meta-modeling technology system, use the modeling language ADSML for automatic instantiation of safety-critical scenarios, and construct a library of safety-critical scenarios, aiming to provide a feasible approach for the modeling of such safety-critical scenarios. Combined with the scenarios of lane changing and overtaking, the effectiveness of the meta-modeling method for autonomous driving safety scenarios driven by spatio-temporal trajectory data is demonstrated, which lays a solid foundation for the construction, simulation, and analysis of the model.

Abstract:With the rapid development of software techniques, domain-driven software raises new challenges in software security and robustness. Symbolic execution and fuzzing have been rapidly developed in recent decades, demonstrating their ability in detecting software bugs. Enormous detected and fixed bugs prove the feasibility of the two methods. However, it is still a challenging task to combine the two methods due to their respective weaknesses. State-of-the-art techniques focus on incorporating the two methods such as using symbolic execution to solve paths when fuzzing gets stuck in complex paths. Unfortunately, such methods are inefficient because they have to switch to fuzzing (resp. symbolic execution) when performing symbolic execution (resp. fuzzing). This paper presents a novel deep learning-based hybrid testing method using symbolic execution and fuzzing. The method tries to predict paths that are suitable for fuzzing (resp. symbolic execution) and use the fuzzing (resp. symbolic execution) to reach the paths. To further enhance effectiveness, this paper also proposes a hybrid mechanism to make them interact with each other. The proposed approach is evaluated on the programs in LAVA-M, and the results are compared with those in the case of using symbolic execution or fuzzing independently. It achieves more than 20\% increase in branch coverage and 1 to 13 times increase in the path number and uncovers 929 more bugs.

Abstract:In software development, developers often need to change or update lost of similar codes. How to perform code transformation automatically has become a research hotspot in software engineering. An effective way is extracting the modification pattern from a set of similar code changes and applying it to automatic code transformation. In the related work, deep-learning-based approaches have achieved much progress, but they suffer from the problem of significant long-term dependency between the code. To address this challenge, an automatic code transformation method is proposed, namely ExpTrans. Based on the graph-based representations of code changes, ExpTrans is enhanced with the structural information of code. It labels the dependency between variables in code parsing and adopts the graph convolutional network and Transformer structure to capture the long-term dependency between the code. ExpTrans is first compared with existing learning-based approaches to evaluate its effectiveness; the results show that ExpTrans gains 11.8%--30.8% precision increment. Then, it is compared with rule-based approaches and the results demonstrate that ExpTrans significantly improves the correct rate of the modified instances.