Infrastructure as Code (IaC) empowers software developers and operations teams to automate the deployment and management of IT infrastructure through code. This is particularly valuable for continuously released deployments such as microservices and cloud-based systems. IaC technologies offer flexibility in provisioning and deploying application architectures. However, if the structure is not well-designed, it can lead to severe issues related to coupling aspects. Unfortunately, a lack of comprehensive coupling guidelines for IaC makes ensuring adherence to best practices challenging. Leveraging IaC-based models, metrics, and source code can enhance the comprehension and implementation of coupling measures. Our objective was to investigate how developers understand information derived from system source code and compare it to formal IaC system diagrams and metrics. We conducted a controlled experiment involving a group of participants to evaluate the understandability of IaC system architecture descriptions through source code inspection and formal representations. We hypothesized that providing formal IaC system diagrams and metrics as supplementary materials would improve the understanding of IaC coupling-related practices measured by task correctness . We also expected that these supplementary resources would lead to a significant increase in task duration and that there would be a notable correlation between correctness and duration . The results suggest that including formal IaC system diagrams and metrics as supplementary materials significantly enhances the comprehension of IaC coupling-related practices, as indicated by task correctness . Moreover, providing these formal representations does not significantly prolong task duration , indicating that they do not hinder understanding. A substantial correlation between task correctness and duration is evident when formal IaC system diagrams and metrics are available.

Models of software systems are used throughout the software development lifecycle. Dataflow diagrams (DFDs), in particular, are well-established resources for security analysis. Many techniques, such as threat modelling, are based on DFDs of the analysed application. However, their impact on the performance of analysts in a security analysis setting has not been explored before. In this paper, we present the findings of an empirical experiment conducted to investigate this effect. Following a within-groups design, participants were asked to solve security-relevant tasks for a given microservice application. In the control condition, the participants had to examine the source code manually. In the model-supported condition, they were additionally provided a DFD of the analysed application and traceability information linking model items to artefacts in source code. We found that the participants (n = 24) performed significantly better in answering the analysis tasks correctly in the model-supported condition (41 % increase in analysis correctness). Further, participants who reported using the provided traceability information performed better in giving evidence for their answers (315% increase in correctness of evidence). Finally, we identified three open challenges of using DFDs for security analysis based on the insights gained in the experiment.

Microservice architectures are widely used today to implement distributed systems. Securing microservice architectures is challenging because of their polyglot nature, continuous evolution, and various security concerns relevant to such architectures. This article proposes a novel, model-based approach providing detection strategies to address the automated detection of security tactics (or patterns and best practices) in a given microservice architecture decomposition model. Our novel detection strategies are metrics-based rules that decide conformance to a security recommendation based on a statistical predictor. The proposed approach models this recommendation using Architectural Design Decisions (ADDs). We apply our approach for four different security-related ADDs on access management, traffic control, and avoiding plaintext sensitive data in the context of microservice systems. We then apply our approach to a model data set of 10 open-source microservice systems and 20 variants of those systems. Our results are detection strategies showing a very low bias, a very high correlation, and a low prediction error in our model data set.

Microservice architectures are increasingly being used to develop application systems. Despite many guidelines and best practices being published, architecting microservice systems for security is challenging. Reasons are the size and complexity of microservice systems, their polyglot nature, and the demand for the continuous evolution of these systems. In this context, to manually validate that security architecture tactics are employed as intended throughout the system is a time-consuming and error-prone task. In this article, we present an approach to avoid such manual validation before each continuous evolution step in a microservice system, which we demonstrate using three widely used categories of security tactics: secure communication, identity management, and observability. Our approach is based on a review of existing security guidelines, the gray literature, and the scientific literature, from which we derived Architectural Design Decisions (ADDs) with the found security tactics as decision options. In our approach, we propose novel detectors to detect these decision options automatically and formally defined metrics to measure the conformance of a system to the different options of the ADDs. We apply the approach to a case study data set of 10 open source microservice systems, plus another 20 variants of these systems, for which we manually inspected the source code for security tactics. We demonstrate and assess the validity and appropriateness of our metrics by performing an assessment of their conformance to the ADDs in our systems’ dataset through statistical methods.