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首页> 外文会议>IEEE International Conference on Software Maintenance and Evolution >The Utility Challenge of Privacy-Preserving Data-Sharing in Cross-Company Defect Prediction: An Empirical Study of the CLIFFMORPH Algorithm
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The Utility Challenge of Privacy-Preserving Data-Sharing in Cross-Company Defect Prediction: An Empirical Study of the CLIFFMORPH Algorithm

机译：跨公司缺陷预测中隐私数据共享的实用挑战：悬崖和变形算法的实证研究

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摘要

In practice, the data owners of source projects may need to share data without disclosing sensitive information. Therefore, privacy-preserving data-sharing becomes an important topic in cross-company defect prediction (CCDP). In this context, the challenge is how to achieve a high privacy-preserving level while ensuring the utility of the shared privatized data for CCDP. CLIFF&MORPH is a recently proposed state-of-the-art privacy-preserving data-sharing algorithm for CCDP. It has been reported that the CLIFF&MORPH CCDP model produces a promising defect prediction performance. However, we find that ManualDown, a simple (unsupervised) module size model, built on the target projects has a comparable or even better defect prediction performance. Since ManualDown does not require any source project data to build the model, it is free of the privacy-preserving data-sharing challenges for CCDP. This means that, for practitioners, the motivation of applying privacy-preserving data-sharing algorithms to CCDP could not be well justified if the utility challenge is not addressed. We analyze the implications of our findings and outline the directions for future research. In particular, we strongly suggest that future studies at least use ManualDown as a baseline model for comparison to help develop practical privacy-preserving data-sharing algorithms for CCDP.
机译：在实践中，源项目的数据所有者可能需要在不公开敏感信息的情况下共享数据。因此，保留的数据共享成为跨公司缺陷预测（CCDP）中的一个重要主题。在这种情况下，挑战是如何实现高隐私保存级别，同时确保CCDP共享私有化数据的实用性。 Cliff＆Morph是最近提出的CCDP最先进的隐私保留数据共享算法。据报道，悬崖和变形CCDP模型产生了有希望的缺陷预测性能。但是，我们发现，在目标项目上构建的简单（无监督）模块大小模型，具有可比或甚至更好的缺陷预测性能。由于手动下降不需要任何源项目数据来构建模型，因此它没有保留CCDP的隐私数据共享挑战。这意味着，对于从业者，如果没有解决实用程序挑战，从业者将保护隐私数据共享算法应用于CCDP的动机无法理解。我们分析了我们的调查结果的含义，概述了未来研究的指示。特别是，我们强烈建议未来的研究至少使用仔细研究作为基线模型，以便帮助开发CCDP的实用隐私保留数据共享算法。

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