Workflow scheduling has been extensively studied in distributed computing, with most research primarily focusing on single workflow scheduling problems. However, in real-world scenarios, multiple workflows from different individual users often need to be scheduled concurrently on shared computing resources, which raises significant fairness concerns among these workflows. Existing approaches typically overlook fairness in multiple workflow scheduling, leading to disproportionate completion time slowdowns across different workflows. To address this challenge, we introduce a novel fairness metric that quantitatively captures the slowdown disparity among multiple workflows and propose the FairFlowSR (Fair-Flow Stochastic Ranking) algorithm to ensure fairness among concurrent workflows. The FairFlowSR algorithm integrates two key components: a fair selection strategy that balances exploitation and exploration, and a stochastic ranking method that effectively handles fairness constraints. Extensive experimental results demonstrate that FairFlowSR significantly outperforms state-of-the-art algorithms, achieving superior fairness maintenance and competitive makespan optimization. These results validate the effectiveness of our approach in achieving a balanced trade-off between efficiency and fairness in multiple-workflow scheduling scenarios.

Testing Autonomous Driving Systems (ADS) is critical to ensure their safety and reliability in dynamic and unpredictable real-world driving environments.In the literature, many scenario-based ADS testing solutions have been proposed to generate safety-critical driving scenarios.Along a similar research line, in this paper, we present a tool, named LiveTCM, which has a web-based model editor for specifying and executing Test Case Specifications (TCS).LiveTCM also has an extensible engine for enabling generation of TCS via real-time communication with the ADS (i.e., the system under test) situated in a simulated ADS driving environment.Videos illustrating the capabilities of LiveTCM can be found at: https://github.com/WSE-Lab/LiveTCM.

Hyperledger Fabric is another development of blockchain technology after Ethereum, which is more suitable as an operating platform for smart contracts. However, the testing technology of Hyperledger Fabric smart contracts (also known as chaincode) is not yet mature currently. Based on this, this paper studies the vulnerability detection of Golang chaincodes. Firstly, we summarize 17 kinds of Golang chaincode vulnerabilities by investigating existing research. Secondly, taking the high accuracy of dynamic detection and the high efficiency of static detection into consideration, we propose a chaincode vulnerability detection framework that combines the dynamic symbolic execution and the static abstract syntax tree analysis technology. We also implement a supporting-tool that can detect the above 15 types of vulnerabilities. Finally, we test the tool by 15 chaincodes collected from GitHub and unknown vulnerabilities were detected in 13 projects. The precision turned out to be 91% after manual inspection. In order to verify the recall rate, we manually inject 30 vulnerabilities into the collected chaincodes and all of them are detected. The evaluation results show the accuracy of the proposed vulnerability detection method for Hyperledger Fabric smart contracts.

With its unique advantages such as decentralization and immutability, blockchain technology has been widely used in various fields in recent years. The smart contract running on the blockchain is also playing an increasingly important role in decentralized application scenarios. Therefore, the automatic detection of security vulnerabilities in smart contracts has become an urgent problem in the application of blockchain technology. Hyperledger Fabric is a smart contract platform based on enterprise-level licensed distributed ledger technology. However, the research on the vulnerability detection technology of Hyperledger Fabric smart contracts is still in its infancy. In this paper, we propose HFContractFuzzer, a method based on Fuzzing technology to detect Hyperledger Fabric smart contracts, which combines a Fuzzing tool for golang named go-fuzz and smart contracts written by golang. We use HFContractFuzzer to detect vulnerabilities in five contracts from typical sources and discover that four of them have security vulnerabilities, proving the effectiveness of the proposed method.