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Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project

机译：开源项目中基于集合技术的软件故障预测

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摘要

Software engineering repositories have been attracted by researchers to mine useful information about the different quality attributes of the software. These repositories have been helpful to software professionals to efficiently allocate various resources in the life cycle of software development. Software fault prediction is a quality assurance activity. In fault prediction, software faults are predicted before actual software testing. As exhaustive software testing is impossible, the use of software fault prediction models can help the proper allocation of testing resources. Various machine learning techniques have been applied to create software fault prediction models. In this study, ensemble models are used for software fault prediction. Change metrics-based data are collected for an open-source android project from GIT repository and code-based metrics data are obtained from PROMISE data repository and datasets kc1, kc2, cm1, and pc1 are used for experimental purpose. Results showed that ensemble models performed better compared to machine learning and hybrid search-based algorithms. Bagging ensemble was found to be more effective in the prediction of faults in comparison to soft and hard voting.
机译：软件工程存储库已被研究人员吸引，以挖掘有关软件不同质量属性的有用信息。这些存储库有助于软件专业人员在软件开发的生命周期中有效地分配各种资源。软件故障预测是质量保证活动。在故障预测中，在实际软件测试之前预测软件故障。随着详尽的软件测试是不可能的，软件故障预测模型的使用可以帮助正确分配测试资源。已应用各种机器学习技术来创建软件故障预测模型。在本研究中，集合模型用于软件故障预测。从GIT存储库中收集基于Metrics的数据，并从Git存储库和基于代码的度量数据获取，从承诺数据存储库和数据集KC1，KC2，CM1和PC1获得实验目的。结果表明，与基于机器学习和混合搜索的算法相比，集合模型更好。与柔软和柔软的投票相比，发现袋装集合在预测故障方面更有效。

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