Data In Brief _Code Smells_Metrics_Fault_ Data_ECLIPSE

For the development of prediction model the association between metrics, code smells and faulty classes in post release object oriented open source systems (Eclipse IDE) is examined. An array of metrics is used as independent variables which includes diverse characteristics of design. Two type of cataloging for code smells - Class Level and Method Level are performed with a set of eight code smells as dependent variables. Reverse engineering code smell predictor application - iPlasma was used which was able to detect the selected code smells and Object Oriented metrics. The perceptiveness of the model on the whole is considered to be of fair to good quality and the model qualifies to be called as a successful model which may require further performance tuning in terms of data and algorithm parameters.

本研究旨在开发预测模型，探讨面向对象开源系统（Eclipse IDE）在发布后，各项指标与代码异味、故障类之间的关联性。研究中采用一系列指标作为自变量，这些指标涵盖了设计的多种特性。针对代码异味，分别从类级和方法级两个层面进行分类，并以八种代码异味作为因变量。本研究采用了逆向工程代码异味预测应用——iPlasma，该应用能够检测选定的代码异味和面向对象指标。整体而言，该模型的感知度处于良好至优秀水平，且模型具备成功模型的资格，可能需要进一步对数据和算法参数进行调整以优化性能。