Prediction of faulty modules in the software development cycle earlier helps to reduce the cost of software development. Test engineers give more attention to the faulty modules to remove any latent defect in the software. Most of the studies available in literature have used historical data related to the same projects for identification of faulty modules; however availability of historical data for new software projects is not possible. In case of new software projects, data for defect prediction is taken from similar types of projects developed earlier and this technique of defect prediction is called cross project defect prediction. In this study applicability of hybrid search based algorithms for cross project defect prediction is investigated. Performance of hybrid search based algorithms is compared for with-in and cross project defect prediction. Hybrid search based algorithms combine the advantages of search based algorithms with machine learning techniques. Results showed that hybrid search based algorithms are more suitable in case of cross project defect prediction in comparison to with-in project defect prediction.

中文翻译：

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使用基于混合搜索的算法进行跨项目缺陷预测

较早地预测软件开发周期中的故障模块有助于降低软件开发成本。测试工程师会更多地关注故障模块，以消除软件中的任何潜在缺陷。文献中的大多数研究都使用与相同项目有关的历史数据来识别故障模块。但是无法获得新软件项目的历史数据。对于新的软件项目，用于缺陷预测的数据取自较早开发的类似类型的项目，这种缺陷预测技术称为跨项目缺陷预测。在这项研究中，研究了基于混合搜索的算法在跨项目缺陷预测中的适用性。比较了基于混合搜索的算法的性能，可用于内部和跨项目缺陷预测。基于混合搜索的算法将基于搜索的算法的优势与机器学习技术相结合。结果表明，与项目内缺陷预测相比，基于混合搜索的算法更适合跨项目缺陷预测。