The smart contract technology has increasingly attracted the attention of different industries. However, a significant number of smart contracts deployed in practice suffer from several bugs, which enable malicious users to cause damage. The research community has shifted their focus to verifying the correctness of smart contracts using model checkers and formal verification methods. The majority of the research investigates the correctness of systems built on one smart contract. This paper proposes a verification approach for systems composed of interacting smart contracts developed and controlled by different entities. We use the NuSMV model checker and the Behavioral Interaction Priority tool to model the behaviors of smart contracts and their interactions with the aim of verifying their compliance with the systems' functional requirements. These requirements are formalized by Linear Temporal Logic propositions. The applicability of our approach is illustrated using a case study from The American Petroleum Institute and implemented using Hyperledger Fabric.

Computer programs (code) are integral to the functions of current society. Yet, little is known about why programmers trust code they did not create. The current paper applied the heuristic-systematic model (HSM) of information processing to perceptions of code trust for reuse. The studies explored transparency (readability and organization) and reputation (source) as factors that influenced trust perceptions and time spent reviewing code using professional programmers. Source and readability manipulations led to higher trustworthiness assessments in the first study. Organization had nonlinear effects on trustworthiness. A three-way interaction including time was also found. The second online study largely replicated the first study’s main and interaction effects for trustworthiness, but the main effects on time were not significant. Our findings suggest the relationships of transparency on trustworthiness are not as straightforward as previously thought. Additionally, the findings of the current study expand the HSM to trust in code.

Cloud-native computing enhances the deployment of microservice architecture (MSA) applications by improving scalability and resilience, particularly in Beyond 5G (B5G) environments such as Sixth-Generation (6G) networks. This is achieved through the ability to replace traditional hardware dependencies with software-defined solutions. While service meshes enable secure communication for deployed MSAs, they struggle to identify vulnerabilities inherent to microservices. The reliance on third-party libraries and modules, essential for MSAs, introduces significant supply chain security risks. Implementing a zero-trust approach for MSAs requires robust mechanisms to continuously verify and monitor the software supply chain of deployed microservices. However, existing service mesh solutions lack runtime trust evaluation capabilities for continuous vulnerability assessment of third-party libraries and modules. This paper introduces a mechanism for continuous runtime trust evaluation of microservices, integrating vulnerability assessments within a service mesh to enhance the deployed MSA application. The proposed approach dynamically assigns trust scores to deployed microservices, rewarding secure practices such as timely vulnerability patching. It also enables the sharing of assessment results, enhancing mitigation strategies across the deployed MSA application. The mechanism is evaluated using the Train Ticket MSA, a complex open-source benchmark MSA application deployed with Docker containers, orchestrated using Kubernetes, and integrated with the Istio service mesh. Results demonstrate that the enhanced service mesh effectively supports dynamic trust evaluation based on the vulnerability posture of deployed microservices, significantly improving MSA security and paving the way for future self-adaptive solutions.

Automation and autonomous systems are quickly becoming a more engrained aspect of modern society. The need for effective, secure computer code in a timely manner has led to the creation of automated code repair techniques to resolve issues quickly. However, the research to date has largely ignored the human factors aspects of automated code repair. The current study explored trust perceptions, reuse intentions, and trust intentions in code repair with human generated patches versus automated code repair patches. In addition, comments in the headers were manipulated to determine the effect of the presence or absence of comments in the header of the code. Participants were 51 programmers with at least 3 years’ experience and knowledge of the C programming language. Results indicated only repair source (human vs. automated code repair) had a significant influence on trust perceptions and trust intentions. Specifically, participants consistently reported higher levels of perceived trustworthiness, intentions to reuse, and trust intentions for human referents compared to automated code repair. No significant effects were found for comments in the headers.

Open-source software (OSS) is a key aspect of software creation. However, little is known about programmers’ decisions to trust software from OSS websites. The current study emulated OSS websites and manipulated reputation and performance factors in the stimuli according to the heuristic-systematic processing model. We sampled professional programmers—with a minimum experience of three years—from Amazon Mechanical Turk (N = 38). We used a 3 × 3 within-subjects design to investigate the relationship between OSS reputation and performance on users’ time spent on code, the number of interface clicks, trustworthiness perceptions, and willingness to use OSS code. We found that participants spent more time on and clicked the interface more often for code that was high in reputation. Meta-information included with OSS tools was found to affect the degree to which computer programmers interact with and perceive online code repositories. Furthermore, participants reported higher levels of perceived trustworthiness in and trust toward highly reputable OSS code. Notably, we observed fewer significant main effects for the performance manipulation, which may correspond to participants considering performance attributes mainly within the context of reputation-relevant information. That is, the degree to which programmers investigate and then trust OSS code may depend on the initial reputation ratings.