A Software Maintainability Dataset

This dataset contains 304 manual evaluations of class-level software maintainability. It can be used to develop and evaluate automated quality prediction tools.<br>This archive was created along the work described in detail in<em>M. Schnappinger, A. Fietzke, and A. Pretschner, "</em><i>Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability</i><em>", International Conference on Software Maintenance and Evolution (ICSME), 2020</em><em><br></em>If you use this dataset in your research, please cite both this dataset and the corresponding paper. This archive containsA readme with all relevant information aboutStudy objectsLabel definitionThreats to validityHints for using the datasetList of metrics used to prioritize the samplesThe code of the study objectsA .csv file containing the readability, understandability, complexity, adequate size, and overall maintainability labelsThe original publicationFigshare uses a strict character limit for this description. Please refer to the `Readme.md` for further information.<br>本数据集包含304份针对类级软件可维护性的人工评估结果，可用于开发及评测自动化软件质量预测工具。 本归档文件的构建基于M. Schnappinger、A. Fietzke与A. Pretschner在2020年国际软件维护与演化会议（ICSME）上发表的详细研究工作，论文题为《软件可维护性数据集的构建：收集、整合与分析专家对软件可维护性的评估》。 若您在研究中使用本数据集，请同时引用本数据集与对应的学术论文。 本归档包含以下内容：一份涵盖所有相关信息的自述文件，其中包含研究对象、标签定义、有效性威胁、数据集使用指南、用于样本优先级排序的指标列表；研究对象的源代码；一份包含可读性、可理解性、复杂度、合理规模与整体可维护性标签的CSV文件；以及原始发表论文。 Figshare 对本描述存在严格字符限制，更多详细信息请参阅自述文件 `Readme.md`。