Context:

Various types of artifacts (requirements, source code, test cases, documents, etc.) are produced throughout the lifecycle of a software. These artifacts are connected with each other via traceability links that are stored in modern application lifecycle management repositories. Throughout the lifecycle of a software, various types of changes can arise in any one of these artifacts. It is important to review such changes to minimize their potential negative impacts. To make sure the review is conducted properly, the reviewer(s) should be chosen appropriately.

Objective:

We previously introduced a novel approach, named RSTrace, to automatically recommend reviewers that are best suited based on their familiarity with a given artifact. In this study, we introduce an advanced version of RSTrace, named RSTrace+ that accounts for recency information of traceability links including practical tool support for GitHub.

Methods:

In this study, we conducted a series of experiments on finding the appropriate code reviewer(s) using RSTrace+ and provided a comparison with the other code reviewer recommendation approaches.

Results:

We had initially tested RSTrace+ on an open source project (Qt 3D Studio) and achieved a top-3 accuracy of 0.89 with an MRR (mean reciprocal ranking) of 0.81. In a further empirical evaluation of 40 open source projects, we compared RSTrace+ with Naive-Bayes, RevFinder and Profile based approach, and observed higher accuracies on the average.

Conclusion:

We confirmed that the proposed reviewer recommendation approach yields promising top-k and MRR scores on the average compared to the existing reviewer recommendation approaches. Unlike other code reviewer recommendation approaches, RSTrace+ is not limited to recommending reviewers for source code artifacts and can potentially be used for recommending reviewers for other types of artifacts. Our approach can also visualize the affected artifacts and help the developer to make assessments of the potential impacts of change to the reviewed artifact.

中文翻译：

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RSTrace +：使用软件工件可追溯性图的审阅者建议

内容：

在软件的整个生命周期中都会产生各种类型的工件（需求，源代码，测试用例，文档等）。这些工件通过存储在现代应用程序生命周期管理存储库中的可追溯性链接相互连接。在软件的整个生命周期中，这些工件中的任何一种都可能发生各种类型的更改。重要的是要检查这些更改，以最大程度地减少其潜在的负面影响。为了确保审查适当地进行，审稿人（一个或多个）应适当选择。

目的：

我们之前曾介绍过一种名为RSTrace的新颖方法，可以根据对特定工件的熟悉程度自动推荐最适合的审阅者。在本研究中，我们介绍了RSTrace的高级版本，称为RSTrace +，该版本说明了可追溯性链接的最新信息，包括对GitHub的实用工具支持。

方法：

在这项研究中，我们进行了一系列使用RSTrace +查找合适的代码审查者的实验，并与其他代码审查者推荐方法进行了比较。

结果：

我们最初在一个开源项目（Qt 3D Studio）上测试了RSTrace +，并获得了0.89的前三名准确性和0.81的MRR（平均倒数排名）。在对40个开源项目的进一步实证评估中，我们将RSTrace +与Naive-Bayes，RevFinder和基于Profile的方法进行了比较，并观察到平均精度更高。

结论：

我们确认，与现有的审阅者推荐方法相比，拟议的审阅者推荐方法平均产生可观的top-k和MRR分数。与其他代码审阅者推荐方法不同，RSTrace +不仅限于为源代码工件推荐审阅者，还可以潜在地用于为其他类型的工件推荐审阅者。我们的方法还可以可视化受影响的工件，并帮助开发人员评估更改对审阅的工件的潜在影响。