Context

Software defect prediction is an important technique that can help practitioners allocate their quality assurance efforts. In recent years, just-in-time (JIT) defect prediction has attracted considerable interest, as it enables developers to identify risky changes at check-in time.

Objective

Many studies have conducted research from supervised and unsupervised perspectives. A model that does not rely on label information would be preferred. However, the performance of unsupervised models proposed by previous studies in the classification scenario was unsatisfactory due to the lack of supervised information. Furthermore, most supervised models fail to outperform simple unsupervised models in the ranking scenario. To overcome this weakness, we conduct research from the semi-supervised perspective that only requires a small quantity of labeled data for training.

Method

In this paper, we propose a semi-supervised model for JIT defect prediction named Effort-Aware Tri-Training (EATT), which is an effort-aware method using a greedy strategy to rank changes. We compare EATT with the state-of-the-art supervised and unsupervised models with respect to different labeled rate.

Results

The experimental results on six open-source projects demonstrate that EATT outperforms existing supervised and unsupervised models for effort-aware JIT defect prediction, and has similar or superior performance in classifying defect-inducing changes.

Conclusion

The results show that EATT can not only achieve high classification accuracy as supervised models, but also offer more practical value than other compared models from the perspective of the effort needed to review changes.

中文翻译：

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努力意识的半监督实时缺陷预测

语境

软件缺陷预测是一项重要的技术，可以帮助从业人员分配质量保证工作。近年来，即时（JIT）缺陷预测吸引了相当大的兴趣，因为它使开发人员能够在签入时识别出风险变化。

目的

许多研究从有监督和无监督的角度进行了研究。不依赖标签信息的模型将是首选。然而，由于缺乏监督信息，先前研究提出的非监督模型在分类场景中的表现并不令人满意。此外，在排名方案中，大多数监督模型都无法胜过简单的无监督模型。为了克服这一弱点，我们从半监督的角度进行研究，该研究仅需要少量标记数据即可进行培训。

方法

在本文中，我们提出了一种名为Effort-Aware Tri-Training（EATT）的半监督的JIT缺陷预测模型，该模型是一种使用贪婪策略对变化进行排序的工作量感知方法。对于不同的标记率，我们将EATT与最新的有监督和无监督模型进行了比较。

结果

在六个开源项目上的实验结果表明，EATT优于现有的可监督的JIT缺陷预测的受监督模型和无监督模型，并且在归类引起缺陷的更改方面具有相似或优异的性能。

结论

结果表明，EATT不仅可以实现作为监督模型的高分类精度，而且从审查更改所需的工作角度来看，它比其他比较模型也具有更大的实用价值。