NeedFeed: taming change notifications by modeling code relevance
NeedFeed: taming change notifications by modeling code relevance
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NeedFeed：通过建模代码相关性来控制更改通知
DOI：
10.1145/2642937.2642985
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Vibha Sinha
中科院分区：
文献类型：
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作者：
Rohan Padhye;Senthil Mani;Vibha Sinha
大多数软件开发工具使开发人员可以订阅有关其团队成员签名的代码的通知，以查看对他们负责的工件的更改。但是，过去的用户研究表明，这种机制是适得其反的，因为开发人员花费了很大的努力在此类提要上寻找与它们相关的项目。我们介绍了需求Feed，该系统通过挖掘项目的软件存储库来建模相关性，并突出显示开发人员可能需要审查的更改。我们评估了几种技术以建模代码相关性，从基于幼稚的触摸方法到使用文件和方法级粒度的时间代码指标的基于通用历史记录的分类器，然后通过使用基于文本的特定模型来改进这些模型，从而改善这些指标。提交消息。需求feed平均将通知混乱量减少了90％以上，最佳策略给出了平均精度和召回率超过75％。
Most software development tools allow developers to subscribe to notifications about code checked-in by their team members in order to review changes to artifacts that they are responsible for. However, past user studies have indicated that this mechanism is counter-productive, as developers spend a significant amount of effort sifting through such feeds looking for items that are relevant to them. We present NeedFeed, a system that models code relevance by mining a project's software repository and highlights changes that a developer may need to review. We evaluate several techniques to model code relevance, from a naive TOUCH-based approach to generic HISTORY-based classifiers using temporal code metrics at file and method-level granularities, which are then improved by building developer-specific models using TEXT-based features from commit messages. NeedFeed reduces notification clutter by more than 90%, on average, with the best strategy giving an average precision and recall of more than 75%.