The importance of architectural knowledge (AK) management for software development has been highlighted over the past ten years, where a significant amount of research has been done. Since the first systems using design rationale in the seventies and eighties to the more modern approaches using AK for designing software architectures, a variety of models, approaches, and research tools have leveraged the interests of researchers and practitioners in AK management (AKM). Capturing, sharing, and using AK has many benefits for software designers and maintainers, but the cost to capture this relevant knowledge hampers a widespread use by software companies. However, as the improvements made over the last decade didn't boost a wider adoption of AKM approaches, there is a need to identify the successes and shortcomings of current AK approaches and know what industry needs from AK. Therefore, as researchers and promoters of many of the AK research tools in the early stages where AK became relevant for the software architecture community, and based on our experience and observations, we provide in this research an informal retrospective analysis of what has been done and the challenges and trends for a future research agenda to promote AK use in modern software development practices.

Today's industrial control systems store large amounts of monitored sensor data in order to optimize industrial processes. In the last decades, architects have designed such systems mainly under the assumption that they operate in closed, plant-side IT infrastructures without horizontal scalability. Cloud technologies could be used in this context to save local IT costs and enable higher scalability, but their maturity for industrial applications with high requirements for responsiveness and robustness is not yet well understood. We propose a conceptual architecture as a basis to designing cloud-native monitoring systems. As a first step we benchmarked three open source time-series databases (OpenTSDB, KairosDB and Databus) on cloud infrastructures with up to 36 nodes with workloads from realistic industrial applications. We found that at least KairosDB fulfills our initial hypotheses concerning scalability and reliability.

Determining the trade-off between performance and costs of a distributed software system is important as it enables fulfilling performance requirements in a cost-efficient way. The large amount of design alternatives for such systems often leads software architects to select a suboptimal solution, which may either waste resources or cannot cope with future workloads. Recently, several approaches have appeared to assist software architects with this design task. In this paper, we present a case study applying one of these approaches, i.e. PerOpteryx, to explore the design space of an existing industrial distributed software system from ABB. To facilitate the design exploration, we created a highly detailed performance and cost model, which was instrumental in determining a cost-efficient architecture solution using an evolutionary algorithm. The case study demonstrates the capabilities of various modern performance modeling tools and a design space exploration tool in an industrial setting,provides lessons learned, and helps other software architects in solving similar problems.

In contrast to software architecture models, architectural decisions are often not explicitly documented, and therefore eventually lost. This contributes to major problems such as high-cost system evolution, stakeholders mis-communication, and limited reusability of core system assets. An approach is outlined that systematically and semi-automatically documents architectural decisions and allows them to be effectively shared by the stakeholders. A first attempt is presented that partially implements the approach by binding architectural decisions, models and the system implementation. The approach is demonstrated with an example demonstrating its usefulness with regards to some industrial use cases.

Software architectures have high costs for change, are complex, and erode during evolution. We believe these problems are partially due to knowledge vaporization. Currently, almost all the knowledge and information about the design decisions the architecture is based on are implicitly embedded in the architecture, but lack a first-class representation. Consequently, knowledge about these design decisions disappears into the architecture, which leads to the aforementioned problems. In this paper, a new perspective on software architecture is presented, which views software architecture as a composition of a set of explicit design decisions. This perspective makes architectural design decisions an explicit part of a software architecture. Consequently, knowledge vaporization is reduced, thereby alleviating some of the fundamental problems of software architecture.