Dataset for "Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs"

This dataset provides materials used and produced in the context of the research study leading to the article <em>Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs</em>. It includes materials used to conduct the study, as well as aggregated and anonymized data produced in its context. We investigated how practitioners perceive the impact of 14 patterns on 7 quality attributes. In particular, we conducted 9 semi-structured interviews to collect industry expertise regarding (1) knowledge and adoption of software patterns, (2) the perceived architectural trade-offs of patterns, and (3) metrics professionals use to measure quality attributes. <strong>Research Objective</strong> Our objective with this work was to obtain insights on the relevance of design patterns in industry, how practitioners perceive their influence on software qualities as a consequence of their usage, and what metrics practitioners use, if any, to determine these derived effects, reflected as software qualities. <strong>Research Questions</strong> <strong>RQ1:</strong> What is the rationale for the adoption of patterns in microservices systems? <strong>RQ2:</strong> How are QAs influenced as a result of applying microservice patterns? <strong>RQ3:</strong> How are QAs measured in microservices? <strong>Interview Artifacts and Results</strong> Materials used to conduct the study: <strong>interview-signup-form.pdf</strong> - Form used by participants to signup for the study. The data collected through this form was used to characterize the participant, and make an early assessment of her or his experience with software development and microservices. <strong>interview-guide.pdf</strong> - Notes used by the researcher to conduct the interviews, including the general structure to follow. <strong>interview-helper.pdf</strong> - Slides used by the researcher during the interviews, to illustrate each of the patterns. Data produced in the context of the study: <strong>PreliminaryTradeoffAnalysis.csv</strong> - Benefits and liabilities gathered by the researchers from the description of the analyzed patterns. Those classified QAs as <em>mixed</em> when we considered that assigning a <em>positive</em> or <em>negative</em> value was a too simplistic judgement. <strong>Demographics.csv</strong> - Demographic data for each of the interviewees. <strong>InterviewsStats.csv</strong> - Simple statistics regarding the interviews, including number of words of the transcripts and duration of the interviews. <strong>Adoptions.csv</strong> - Reported patterns adopted by the interviewees. <strong>Gains.csv</strong> - Gains of each pattern as reported by interviewees. <strong>Pains.csv</strong> - Pains of each pattern as reported by interviewees. <strong>Indicators.csv</strong> - Reported indicators for measuring each QA. <strong>Techniques.csv</strong> - Reported techniques for addressing each QA. <strong>ThirdPartyMonitoringTools.csv</strong> - Reported third-party monitoring tools and the number of participants who use them.本数据集收录了支撑学术论文<em>《微服务系统模式设计：质量权衡的实证研究》（Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs）</em>的相关研究材料，涵盖研究开展过程中使用的各类资料，以及研究产出的聚合匿名化数据。本研究探讨了从业人员如何认知14种软件模式（software patterns）对7种质量属性（quality attributes, QAs）的影响。具体而言，我们开展了9次半结构化访谈，以收集行业专家经验，内容包括：(1) 软件模式的认知与采用情况；(2) 从业者感知到的模式架构权衡；(3) 专业人员用于衡量质量属性的指标。 <strong>研究目标</strong> 本研究的目标在于获取如下洞见：工业界中设计模式的应用价值、从业人员如何认知采用模式对软件质量属性带来的影响，以及从业者若需量化此类由模式应用衍生的软件质量效应时所使用的衡量指标。 <strong>研究问题</strong> <strong>RQ1:</strong> 微服务系统中采用模式的底层逻辑是什么？ <strong>RQ2:</strong> 应用微服务模式会对质量属性（QAs）产生何种影响？ <strong>RQ3:</strong> 微服务场景下如何衡量质量属性？ <strong>访谈相关材料与研究成果</strong> 研究开展过程中使用的材料包括： <strong>interview-signup-form.pdf</strong>：用于招募研究参与者的报名表，通过该表单收集的数据用于刻画参与者特征，并初步评估其软件开发与微服务相关经验。 <strong>interview-guide.pdf</strong>：研究者开展访谈时使用的流程指引文档，包含访谈需遵循的整体框架。 <strong>interview-helper.pdf</strong>：研究者在访谈过程中用于演示各类模式的演示幻灯片。 研究产出的相关数据包括： <strong>PreliminaryTradeoffAnalysis.csv</strong>：研究者从所分析模式的描述中整理得到的收益与弊端。当我们认为将其质量属性（QAs）简单判定为<em>正向（positive）</em>或<em>负向（negative）</em>过于片面时，将其归类为<em>混合（mixed）</em>。 <strong>Demographics.csv</strong>：每位受访参与者的人口统计学数据。 <strong>InterviewsStats.csv</strong>：关于访谈的基础统计数据，包括访谈转录文本的字数与访谈时长。 <strong>Adoptions.csv</strong>：受访参与者报告的已采用模式清单。 <strong>Gains.csv</strong>：受访参与者提及的各模式带来的收益。 <strong>Pains.csv</strong>：受访参与者提及的各模式存在的弊端。 <strong>Indicators.csv</strong>：受访参与者报告的用于衡量各类质量属性的指标。 <strong>Techniques.csv</strong>：受访参与者报告的用于优化各类质量属性的技术手段。 <strong>ThirdPartyMonitoringTools.csv</strong>：受访参与者报告的第三方监控工具及使用对应工具的参与者人数。