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)本复现包包含针对通过拓展文献研究（Soldani, J., & Brogi, A. (2022). 面向（微）服务云应用的异常检测（anomaly detection）与故障根因分析（root cause analysis）：综述. ACM Computing Surveys (CSUR), 55(3), 1-39.）所识别的异常检测方法的一般性说明，以及来自多个领域的15名访谈参与者的相关数据，用于阐述异常、异常检测方法的研究范式与核心发现，同时涵盖从运行时监控（runtime monitoring）数据类型（日志、追踪数据、指标数据）中提取的、用于异常检测的关键监控参数。 出于保密要求，本研究无法提供访谈录像与转录文本。 本复现包包含以下内容： 1. Interview_Guidelines.pdf（访谈指南.pdf）：收录经过预试验优化的访谈问题，分为介绍模块、用例详述模块、解释系统行为的参数与相关提问模块。此外，文件中还附带了针对本次访谈问题设计意图的简短说明。 2. Procedure_Interview_Participant_Selection.pdf（访谈参与者遴选流程.pdf）：阐释了本研究采用的目的性抽样（purposive sampling）遴选策略、用于联系行业访谈对象的邮件模板，以及从访谈中收集的人口统计学信息。 3. RQ1_Inductive_Coding.pdf（研究问题1_归纳编码（inductive coding）.pdf）：汇总了访谈参与者针对异常的定义、特征及行业实践案例的相关表述。 4. RQ1_IEEE_Definitions_over_Years.pdf（研究问题1_历年IEEE异常定义汇总.pdf）：汇总了本研究识别到的历年IEEE关于异常的官方定义。 5. RQ2_Inductive_Coding.pdf（研究问题2_归纳编码（inductive coding）.pdf）：汇总了访谈参与者针对基于规则与基于AI的异常检测方法及其优缺点的相关表述。 6. RQ3_Overview_Interviews_IndustryPaper.xlxs（研究问题3_访谈与行业论文综述.xlsx）：汇总了访谈参与者的相关表述，以及那些采用行业数据集开展方法评估的论文中所使用的异常检测工具，同时收录了适用于异常检测的各类参数。此外，该文件还包含方法论相关信息（如纳入/排除标准（inclusion/exclusion criteria）、已排除论文、基于样本的双盲评审（dual blind review）流程）。 7. Overview_AllPapers.xlxs（全文献综述.xlsx）：汇总了本研究识别到的全部文献，涵盖采用行业数据集与基准数据集（benchmark datasets）开展评估的异常检测方法相关研究。