Applying Cross Project Defect Prediction Approaches to Cross-Company Effort Estimation
Applying Cross Project Defect Prediction Approaches to Cross-Company Effort Estimation
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DOI：
10.1145/3345629.3345638
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
S. Amasaki;Tomoyuki Yokogawa;Hirohisa Aman
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文献类型：
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作者：
S. Amasaki;Tomoyuki Yokogawa;Hirohisa Aman
背景：软件工程中的预测系统经常受到项目中缺乏合适数据的困扰。一个很有前途的解决方案是利用项目外部数据的迁移学习。已经提出了许多用于缺陷预测的迁移学习方法，称为跨项目缺陷预测（CPDP）。与此相反，一些方法已经提出了软件工作量估计称为跨公司软件工作量估计（CCSEE）。CCSEE和CPDP都在研究类似的问题，而CPDP的一些方法在实际应用中也可以作为CCSEE。因此，它是有益的，以提高CCSEE性能，检查如何以及CPDP方法可以执行CCSEE方法。目的：探索CPDP方法如何与CCSEE方法一样工作。方法：在CCSEE中进行实证实验以评估CPDP方法的性能。我们研究了7个CPDP方法，这些方法是由于易于应用而选择的。这些方法被应用到8个数据集，每个数据集由来自不同领域的几个子集组成。估计结果进行了评估与一个共同的性能指标称为SA。结果：有几种CPDP方法可以提高估计精度，但提高的幅度不大。结论：直接应用选定的CPDP方法并没有带来明显的效果。CCSEE可能需要特定的迁移学习方法来进行更多的改进。
BACKGROUND: Prediction systems in software engineering often suffer from the shortage of suitable data within a project. A promising solution is transfer learning that utilizes data from outside the project. Many transfer learning approaches have been proposed for defect prediction known as cross-project defect prediction (CPDP). In contrast, a few approaches have been proposed for software effort estimation known as cross-company software effort estimation (CCSEE). Both CCSEE and CPDP are engaged in a similar problem, and a few CPDP approaches are applicable as CCSEE in actual. It is thus beneficial for improving CCSEE performance to examine how well CPDP approaches can perform as CCSEE approaches. AIMS: To explore how well CPDP approaches work as CCSEE approaches. METHOD: An empirical experiment was conducted for evaluating the performance of CPDP approaches in CCSEE. We examined 7 CPDP approaches which were selected due to the easiness of application. Those approaches were applied to 8 data sets, each of which consists of a few subsets from different domains. The estimation results were evaluated with a common performance measure called SA. RESULTS: there were several CPDP approaches which could improve the estimation accuracy though the degree of improvement was not large. CONCLUSIONS: A straight forward application of selected CPDP approaches did not bring a clear effect. CCSEE may need specific transfer learning approaches for more improvement.