Abstract The explicit consideration of the software architecture supports system evolution and efficient quality assurance. In particular, Architecture-based Performance Prediction (AbPP) assesses the performance for future scenarios (e.g., alternative workload, design, deployment) without expensive measurements for all such alternatives. However, accurate AbPP requires an up-to-date architectural Performance Model (aPM) that is parameterized over factors impacting the performance (e.g., input data characteristics). Especially in agile development, keeping such a parametric aPM consistent with software artifacts is challenging due to frequent evolutionary, adaptive, and usage-related changes. Existing approaches do not address the impact of all aforementioned changes. Moreover, the extraction of a complete aPM after each impacting change causes unnecessary monitoring overhead and may overwrite previous manual adjustments. In this article, we present the Continuous Integration of architectural Performance Model (CIPM) approach, which automatically updates a parametric aPM after each evolutionary, adaptive, or usage change. To reduce the monitoring overhead, CIPM only calibrates the affected performance parameters (e.g., resource demand) using adaptive monitoring. Moreover, a self-validation process in CIPM validates the accuracy, manages the monitoring to reduce overhead, and recalibrates inaccurate parts. Consequently, CIPM will automatically keep the aPM up-to-date throughout the development and operation, which enables AbPP for a proactive identification of upcoming performance problems and for evaluating alternatives at low costs. We evaluate the applicability of CIPM in terms of accuracy, monitoring overhead, and scalability using six cases (four Java-based open source applications and two industrial Lua-based sensor applications). Regarding accuracy, we observed that CIPM correctly keeps an aPM up-to-date and estimates performance parameters well so that it supports accurate performance predictions. Regarding the monitoring overhead in our experiments, CIPM’s adaptive instrumentation demonstrated a significant reduction in the number of required instrumentation probes, ranging from 12.6 % to 83.3 %, depending on the specific cases evaluated. Finally, we found out that CIPM’s execution time is reasonable and scales well with an increasing number of model elements and monitoring data. Graphical Abstract

Microservice-based systems are usually developed according to agile practices like DevOps, which enables rapid and frequent releases to promptly react and adapt to changes. Monitoring is a key enabler for these systems, as they allow to continuously get feedback from the field and support timely and tailored decisions for a quality-driven evolution. In the realm of monitoring tools available for microservices in the DevOps-driven development practice, each with different features, assumptions, and performance, selecting a suitable tool is an as much difficult as impactful task. This article presents the results of a systematic study of the grey literature we performed to identify, classify and analyze the available monitoring tools for DevOps and microservices. We selected and examined a list of 71 monitoring tools, drawing a map of their characteristics, limitations, assumptions, and open challenges, meant to be useful to both researchers and practitioners working in this area. Results are publicly available and replicable. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.