Replication Package - How Industry Tackles Anomalies during Runtime: Approaches and Key Monitoring Parameters

This replication package includes general remarks on anomaly detection approaches identified via an extension of a literature study (Soldani, J., & Brogi, A. (2022). Anomaly detection and failure root cause analysis in (micro) service-based cloud applications: A survey. ACM Computing Surveys (CSUR), 55(3), 1-39.) and 15 interview participants from various domains to address the methodology and findings for anomalies, anomaly detection approaches and key monitoring parameters extracted from runtime monitoring data types (logs, traces, metrics) to detect anomalies. 

Due to confidentiality, we cannot provide the video recordings or transcripts. 

This replication package contains:

Interview_Guidelines.pdf: includes the pilot-tested interview questions split up into introduction, use case elaboration, and parameters that explain system behavior and questions. Furthermore, we include short summaries expressing our intention via the questions

Procedure_Interview_Participant_Selection.pdf: explains the applied purposive sampling selection strategy, the email used to contact industry interview partners and the demographic information collected from the interviews

RQ1_Inductive_Coding.pdf: summarises the interview participants' statements regarding the interpretations and characteristics of an anomaly and industry examples

RQ1_IEEE_Definitions_over_Years.pdf: summary of identified IEEE definitions regarding anomalies

RQ2_Inductive_Coding.pdf: summarises the interview participants' statements regarding rule-based and AI-based and advantages/disadvantages thereof

RQ3_Overview_Interviews_IndustryPaper.xlxs: summarises the interview participants' statements & anomaly detection tools of papers that evaluated their approach with industry datasets, regarding parameters suitable for detecting anomalies. Furthermore, it includes methodological information (such as inclusion/exclusion criteria, excluded papers, and the sample based dual blind review)

Overview_AllPapers.xlxs: summarises all identified literature studies (anomaly detection approaches that are evaluated via industry datasets and via benchmark datasets)