Deviations from expected behavior during runtime, known as anomalies, have become more common due to the systems' complexity, especially for microservices. Consequently, analyzing runtime monitoring data, such as logs, traces for microservices, and metrics, is challenging due to the large volume of data collected. Developing effective rules or AI algorithms requires a deep understanding of this data to reliably detect unfore-seen anomalies. This paper seeks to comprehend anomalies and current anomaly detection approaches across diverse industrial sectors. Additionally, it aims to pinpoint the parameters necessary for identifying anomalies via runtime monitoring data. Therefore, we conducted semi-structured interviews with fifteen industry participants who rely on anomaly detection during runtime. Additionally, to supplement information from the interviews, we performed a literature review focusing on anomaly detection approaches applied to industrial real-life datasets. Our paper (1) demonstrates the diversity of interpretations and examples of software anomalies during runtime and (2) explores the reasons behind choosing rule-based approaches in the industry over self-developed AI approaches. AI-based approaches have become prominent in published industry-related papers in the last three years. Furthermore, we (3) identified key monitoring parameters collected during runtime (logs, traces, and metrics) that assist practitioners in detecting anomalies during runtime without introducing bias in their anomaly detection approach due to inconclusive parameters.

Screencasts and videos with screen recordings are becoming an increasingly popular source of information for users to understand and learn about software applications. However, searching for answers to specific questions in screen videos is notoriously difficult due to the effort for locating specific events of interest and reproducing the application's state up to this event. To increase the efficiency when working with screen videos, we propose a solution for replaying recorded sequences shown in videos directly on live applications. In this paper, we describe the analysis of screen videos to automatically identify and extract user interactions and the construction of visual scripts, which are used to run the application in sync with replaying the video. Currently, a first prototype has been developed to demonstrate the technical feasibility of the approach. The paper provides an overview of the implemented solution concept and discusses technical challenges, open issues, as well as future application scenarios.