Today’s software development is typically driven by incremental changes made to software to implement a new functionality, fix a bug, or improve its performance and security. Each change request is often described as an issue. Recent studies suggest that a set of components (e.g., software modules) relevant to the resolution of an issue is one of the most important information provided with the issue that software engineers often rely on. However, assigning an issue to the correct component(s) is challenging, especially for large-scale projects which have up to hundreds of components. In this paper, we propose a predictive model which learns from historical issue reports and recommends the most relevant components for new issues. Our model uses Long Short-Term Memory, a deep learning technique, to automatically learn semantic features representing an issue report, and combines them with the traditional textual similarity features. An extensive evaluation on 142,025 issues from 11 large projects shows that our approach outperforms one common baseline, two state-of-the-art techniques, and six alternative techniques with an improvement of 16.70%–66.31% on average across all projects in predictive performance.

当今的软件开发通常是由对软件进行的增量更改驱动的，以实现新功能，修复错误或提高其性能和安全性。每个变更请求通常被描述为一个问题。最近的研究表明，与问题的解决相关的一组组件（例如，软件模块）是软件工程师经常依赖的与该问题一起提供的最重要的信息之一。但是，将问题分配给正确的组件是一项挑战，特别是对于具有多达数百个组件的大型项目而言。在本文中，我们提出了一个预测模型，该模型可以从历史问题报告中学习，并为新问题推荐最相关的组件。我们的模型使用了深度学习技术Long Short-Term Memory，自动学习代表问题报告的语义特征，并将其与传统的文本相似性特征相结合。对来自11个大型项目的142,025个问题的广泛评估表明，我们的方法优于一项通用基准，两项最新技术和六项替代技术，所有项目的预测绩效平均提高了16.70％–66.31％ 。