Isolating and Predicting Risks in Architectural Design - Metric Analysis Replication Data

This zip file contains the replication data set for thesis entitled 'Isolating and Predicting Risks in Architectural Design' by Andrew Leigh. To examine the relationship between container level design metrics and risk, a non-experimental quantitative approach that combined causal comparative and correlation design was selected. A causal comparative approach was needed to test the relative performance of the different risk container types, and a correlation design was needed to test the strength of association between the container design metrics calculated for each container type and error-proneness/change propagation.

The causal comparative correlation testing required sources of comparable design and outcome data from software development projects. Outcome data for the maintainability risks selected for testing, error-proneness and change propagation, is collected from source code repositories such as Subversion and Git, and issue tracking systems such as Jira. Section 3.2 of the thesis further expands upon the details of, and rationale behind, the specific project selection criteria as well as explaining how the selected projects meet those criteria. This replication data set contains the data collected for the four projects selected.

本压缩文件包含由 Andrew Leigh 撰写的论文《在建筑设计中隔离和预测风险》的复杂数据集。为探究容器级别设计指标与风险之间的关系，本研究选取了一种结合因果比较设计和相关性设计的非实验性定量方法。因果比较方法被用于测试不同风险容器类型的相对性能，而相关性设计则用于检验针对每种容器类型计算出的设计指标与错误倾向/变更传播强度之间的关联强度。因果比较相关性测试需要来自软件开发项目的可比设计数据和结果数据。针对测试中选定的可维护性风险，即错误倾向和变更传播，其结果数据来源于源代码仓库，如 Subversion 和 Git，以及问题跟踪系统，如 Jira。论文的第 3.2 节进一步阐述了具体项目选择标准的细节及其背后的原因，并解释了所选项目如何满足这些标准。本复杂数据集包含了为所选四个项目收集的数据。