Software defect prediction, aimed at assisting software practitioners in allocating test resources more efficiently, predicts the potential defective modules in software products. With the development of defect prediction technology, the inability of traditional software features to capture semantic information is exposed, hence related researchers have turned to semantic features to build defect prediction models. However, sometimes traditional features such as lines of code (LOC) also play an important role in defect prediction. Most of the existing researches only focus on using a single type of feature as the input of the model. In this article, a defect prediction method based on gated hierarchical long short-term memory networks (GH-LSTMs) is proposed, which uses hierarchical LSTM networks to extract both semantic features from word embeddings of abstract syntax trees (ASTs) of source code files, and traditional features provided by the PROMISE repository. More importantly, we adopt a gated fusion strategy to combine the outputs of the hierarchical networks properly. Experimental results show that GH-LSTMs outperforms existing methods under both noneffort-aware and effort-aware scenarios.

中文翻译：

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基于门控分层 LSTM 的软件缺陷预测

软件缺陷预测，旨在帮助软件从业者更有效地分配测试资源，预测软件产品中潜在的缺陷模块。随着缺陷预测技术的发展，暴露出传统软件特征无法捕捉语义信息的问题，因此相关研究人员转向语义特征来构建缺陷预测模型。但是，有时代码行 (LOC) 等传统特征也在缺陷预测中发挥重要作用。现有的研究大多只关注使用单一类型的特征作为模型的输入。在本文中，提出了一种基于门控分层长短期记忆网络（GH-LSTMs）的缺陷预测方法，它使用分层 LSTM 网络从源代码文件的抽象语法树 (AST) 的词嵌入中提取语义特征，以及 PROMISE 存储库提供的传统特征。更重要的是，我们采用门控融合策略来正确组合分层网络的输出。实验结果表明，GH-LSTMs 在非努力感知和努力感知场景下都优于现有方法。