Hyperledger Fabric (Fabric for short), is a consortium blockchain platform that adopts the smart contract paradigm and provides complete operational functions. Although it has become the system with the highest throughput among open source blockchain systems, its performance cannot meet the needs of industrial-grade application scenarios. To further expand the application scenarios of blockchain, this paper proposes a Transaction Batch Processing Scheduling (TBPS) algorithm for multi-channel Fabric networks based on Lyapunov optimization theory. The algorithm maximizes the consensus efficiency of the system while ensuring the minimum transaction accumulation, and provides stability conditions and optimal performance for the system under transaction batch processing. Finally, we built a blockchain network of Fabric’s latest stable version v 2.0 via the cloud platform, providing an order of magnitude of algorithmic parameters by testing transaction processing rates. To simulate the distribution of performance indicators such as transaction delay, system transaction accumulation and average transaction processing rate under different impact factors, and verify the effectiveness of the proposed TBPS algorithm.

In large-scale, active software projects, one of the main challenges with code review is prioritizing the many Code Review Requests (CRRs) these projects receive. Prior studies have developed many Learning-to-Rank (LtR) models in support of prioritizing CRRs and adopted rich evaluation metrics to compare their performances. However, the evaluation was performed before observing the complex interactions between CRRs and reviewers, activities and activities in real-world code reviews. Such a pre-review evaluation provides few indications about how effective LtR models contribute to code reviews. This study aims to perform a post-review evaluation on LtR models for prioritizing CRRs. To establish the evaluation environment, we employ Discrete-Event Simulation (DES) paradigm-based Software Process Simulation Modeling (SPSM) to simulate real-world code review processes, together with three customized evaluation metrics. We develop seven LtR models and use the historical review orders of CRRs as baselines for evaluation. The results indicate that employing LtR can effectively help to accelerate the completion of reviewing CRRs and the delivery of qualified code changes. Among the seven LtR models, LambdaMART and AdaRank are particularly beneficial for accelerating completion and delivery, respectively. This study empirically demonstrates the effectiveness of using DES-based SPSM for simulating code review processes, the benefits of using LtR for prioritizing CRRs, and the specific advantages of several LtR models. This study provides new ideas for software organizations that seek to evaluate LtR models and other artificial intelligence-powered software techniques.Data&materials: https://figshare.com/s/a033e99cd2a61e64c8bc.