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)本复现包包含基于文献研究扩展得到的异常检测方法相关综述说明（引用文献：Soldani, J., & Brogi, A. (2022). 基于（微）服务的云应用异常检测与故障根因分析综述. ACM计算概览（ACM Computing Surveys, CSUR）, 55(3), 1-39.），以及来自多个领域的15名访谈参与者数据，用于梳理从运行时监控数据类型（日志、追踪数据、性能指标）中提取的异常、异常检测方法及关键监控参数相关的研究方法论与发现。 出于保密要求，本复现包无法提供访谈录像及转录文本。 本复现包包含以下内容： 1. Interview_Guidelines.pdf：包含经过预试验验证的访谈问卷，问卷分为介绍环节、用例详述模块，以及用于阐释系统行为与相关问题的参数说明模块。此外，本文件还附带简短说明，阐述我们通过访谈问题传递的研究意图。 2. Procedure_Interview_Participant_Selection.pdf：说明本研究采用的目的性抽样选择策略、用于联系行业访谈对象的邮件模板，以及从访谈中收集的受访者人口统计学信息。 3. RQ1_Inductive_Coding.pdf：汇总访谈参与者针对异常的解读、特征及行业实例所发表的观点。 4. RQ1_IEEE_Definitions_over_Years.pdf：汇总不同时期IEEE发布的异常相关定义。 5. RQ2_Inductive_Coding.pdf：汇总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所发表的观点。 6. RQ3_Overview_Interviews_IndustryPaper.xlxs：汇总访谈参与者的观点，以及那些采用行业数据集评估自身方法的相关文献中提及的异常检测工具，同时涵盖适用于异常检测的相关参数信息。此外，该文件还包含研究方法相关细节（如纳入/排除标准、排除的文献，以及基于样本的双盲评审流程）。 7. Overview_AllPapers.xlxs：汇总所有已识别的文献研究（包括采用行业数据集与基准数据集评估自身方法的异常检测相关研究）。