ABSTRACT An increasing number of knowledge resources are stored and disseminated in digital form, resulting in new challenges for Intellectual Property Rights (IPR) protection, including difficulty in establishing rights, difficulty in defending rights, and difficulty in incurring high costs. The proposed system in this paper aims to use Internet of Things (IoT) devices to collect knowledge resource data, store it in the Interplanetary File System (IPFS) network, and mint non‐fungible tokens (NFTs) in the blockchain, simplifying the process of IPR confirmation and protection while reducing costs. Additionally, blockchain transactions are delivered to the blockchain service network (BSN) to enhance network credibility. Experimental results show an average file storage time of 0.05 s, a 13% reduction in the average time for property rights registration, and a reduction in maintenance costs.

Trustworthy Open Source Software (OSS) development processes are the basis that secures the long-term trustworthiness of software projects and products. With the aim to investigate the trustworthiness of the Pull Request (PR) process, the common model of collaborative development in OSS community, we exploit process mining to identify and analyze the normal and anomalous patterns of PR processes, and propose our approach to identifying anomalies from both control-flow and semantic aspects, and then to analyze and synthesize the root causes of the identified anomalies. We analyze 17531 PRs of 18 OSS projects on GitHub, extracting 26 root causes of control-flow anomalies and 19 root causes of semantic anomalies. We find that most PRs can hardly contain both semantic anomalies and control-flow anomalies, and the internal custom rules in projects may be the key causes for the identified anomalous PRs. We further discover and analyze the patterns of normal PR processes. We find that PRs in the non-fork model (42%) are far more likely than the fork model (5%) to bypass the review process, indicating a higher potential risk. Besides, we analyzed nine poisoned projects whose PR practices were indeed worse. Given the complex and diverse PR processes in OSS community, the proposed approach can help identify and understand not only anomalous PRs but also normal PRs, which offers early risk indications of suspicious incidents (such as poisoning) to OSS supply chain.

Continuous Integration (CI) enables developers to detect defects early and thus reduce lead time. However, the high frequency and long duration of executing CI have a detrimental effect on this practice. Existing studies have focused on using CI outcome predictors to reduce frequency. Since there is no reported project using predictive CI, it is difficult to evaluate its economic impact. This research aims to investigate predictive CI from a process perspective, including why and when to adopt predictors, what predictors to be used, and how to practice predictive CI in real projects. We innovatively employ Software Process Simulation to simulate a predictive CI process with a Discrete-Event Simulation (DES) model and conduct simulation-based experiments. We develop the Rollback-based Identification of Defective Commits (RIDEC) method to account for the negative effects of false predictions in simulations. Experimental results show that: 1) using predictive CI generally improves the effectiveness of CI, reducing time costs by up to 36.8% and the average waiting time before executing CI by 90.5%; 2) the time-saving varies across projects, with higher commit frequency projects benefiting more; and 3) predictor performance does not strongly correlate with time savings, but the precision of both failed and passed predictions should be paid more attention. Simulation-based evaluation helps identify overlooked aspects in existing research. Predictive CI saves time and resources, but improved prediction performance has limited cost-saving benefits. The primary value of predictive CI lies in providing accurate and quick feedback to developers, aligning with the goal of CI.