识别和描述信息搜索任务

Chris Satterfield, Thomas Fritz, G. Murphy
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引用次数: 1

摘要

软件开发人员每天要处理许多任务,经常在这些任务之间来回切换。这种不断的任务混乱使得开发人员很难知道他们什么时候在做什么任务的细节,使任务恢复、计划、回顾和报告活动变得复杂。在自动化解决这个问题的第一步中,我们介绍了一种新的方法来帮助确定信息查找任务期间的工作主题-软件开发人员面临的最常见的任务类型之一-该方法基于定期捕获开发人员活动窗口的内容并创建开发人员查看的关键信息的矢量表示。为了评估我们的方法,我们创建了一个数据集,其中有多个开发人员在同一组六个信息搜索任务上工作,我们也为其他研究人员提供了研究类似方法的数据集。我们的分析表明,我们的方法可以实现:1)开发人员的工作片段自动与已知任务集中的任务相关联,平均准确率为70.6%;2)描述工作片段的词云,开发人员可以使用它来识别任务,平均准确率为67.9%。
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Identifying and Describing Information Seeking Tasks
A software developer works on many tasks per day, frequently switching between these tasks back and forth. This constant churn of tasks makes it difficult for a developer to know the specifics of when they worked on what task, complicating task resumption, planning, retrospection, and reporting activities. In a first step towards an automated aid to this issue, we introduce a new approach to help identify the topic of work during an information seeking task - one of the most common types of tasks that software developers face - that is based on capturing the contents of the developer's active window at regular intervals and creating a vector representation of key information the developer viewed. To evaluate our approach, we created a data set with multiple developers working on the same set of six information seeking tasks that we also make available for other researchers to investigate similar approaches. Our analysis shows that our approach enables: 1) segments of a developer's work to be automatically associated with a task from a known set of tasks with average accuracy of 70.6%, and 2) a word cloud describing a segment of work that a developer can use to recognize a task with average accuracy of 67.9%.
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