摘要:代码注释是辅助编程人员理解源代码的有效手段之一.高质量的注释决策不仅能够覆盖软件系统中的核心代码片段，还能避免产生多余的代码注释.然而在实际开发中，编程人员往往缺乏统一的注释规范，大部分的注释决策都取决于个人经验以及领域知识.对于新手程序员来说，注释决策显然成为一项重要而艰巨的任务.为了减少编程人员投入过多的精力在注释决策上，从大量的代码注释实例中学习出一种通用的注释决策规范，并提出了CommentAdviser方法，以辅助编程人员在代码开发过程中做出恰当的注释决策.由于注释决策与代码本身的上下文信息密切相关，因此，从当前代码行的上下文代码中提取代码结构特征以及代码语义特征作为支持注释决策的主要依据.然后，利用机器学习算法判定当前代码行是否为可能的注释点.在GitHub中的10个大型开源软件的数据集上评估了所提出的方法，实验结果以及用户调研表明，代码注释决策支持CommentAdviser方法的可行性和有效性.



Abstract:Code comment is quite important to help developer review and comprehend source code. Strategic comment decision is desired to cover core code snippets of software system without incurring unintended trivial comments. However, in current practice, there is a lack of rigorous specifications for developers to make their comment decisions. Commenting has become an important yet tough decision which mostly depends on the personal experience of developers. To reduce the effort on making comment decisions, this paper investigates a unified commenting regulation from a large number of commenting instances. A method, CommentAdviser, is proposed to guide developers in placing comments in source code. Since making comment is closely related to the context information of source code themselves, the method identifies this important factor for determining where to comment and extract them as structural context feature and semantic context feature. Next, machine learning techniques are applied to identify the possible commenting locations in source code. CommentAdviser is evaluated on 10 data sets from GitHub. The experimental results, as well as a user study, demonstrate the feasibility and effectiveness of CommentAdviser.



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