论文标题
部分可观测时空混沌系统的无模型预测
Taming Multi-Output Recommenders for Software Engineering
论文作者
论文摘要
推荐系统是软件工程师的宝贵工具。例如，他们可以为开发人员提供可能包含错误的文件的排名列表，或针对给定方法存根的多个自动完成建议。但是，这些推荐系统与开发人员的互动方式通常是基本的 - 一长串建议仅按照模型的信心进行排名。在本视觉论文中，我们列出了研究议程，以重新想象软件工程的推荐系统如何将其见解传达给开发人员。在发布建议时，我们的目标是推荐多样化而不是冗余解决方案，并以突出其差异的方式呈现它们。我们还希望在努力进行整体端到端评估时进行无缝互动的建议。通过这样做，我们认为推荐系统可以在帮助开发人员编写更好的软件方面发挥更重要的作用。
Recommender systems are a valuable tool for software engineers. For example, they can provide developers with a ranked list of files likely to contain a bug, or multiple auto-complete suggestions for a given method stub. However, the way these recommender systems interact with developers is often rudimentary -- a long list of recommendations only ranked by the model's confidence. In this vision paper, we lay out our research agenda for re-imagining how recommender systems for software engineering communicate their insights to developers. When issuing recommendations, our aim is to recommend diverse rather than redundant solutions and present them in ways that highlight their differences. We also want to allow for seamless and interactive navigation of suggestions while striving for holistic end-to-end evaluations. By doing so, we believe that recommender systems can play an even more important role in helping developers write better software.
