从程序调查活动中自动推断关注代码

M. Robillard, G. Murphy
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引用次数: 70

摘要

当执行程序演化任务时,开发人员通常花费大量的精力调查和重新调查源代码。为了减少这种努力,我们提出了一种技术来自动推断作为一组关注描述的程序调查活动的本质。我们的技术产生的关注描述列出了在研究面向对象系统的上下文中重要的方法和领域。开发人员可以依靠这些信息来执行手头的变更任务,或者在稍后的阶段执行涉及相同关注点的变更。该技术涉及将算法应用于程序调查会话的记录。该记录列出了开发人员在调查程序时访问了哪些源代码片段,以及如何访问不同的代码片段。我们将该技术应用于从涉及两个不同程序演变任务的五个受试者的程序调查活动中获得的数据。结果表明,相关的问题可以通过一个可控的噪音水平来识别。
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Automatically inferring concern code from program investigation activities
When performing a program evolution task, developers typically spend a significant amount of effort investigating and reinvestigating source code. To reduce this effort, we propose a technique to automatically infer the essence of program investigation activities as a set of concern descriptions. The concern descriptions produced by our technique list methods and fields of importance in the context of the investigation of an object-oriented system. A developer can rely on this information to perform the change task at hand, or at a later stage for a change that involves the same concerns. The technique involves applying an algorithm to a transcript of a program investigation session. The transcript lists which pieces of source code were accessed by a developer when investigating a program and how the different pieces of code were accessed. We applied the technique to data obtained from program investigation activities for five subjects involved in two different program evolution tasks. The results show that relevant concerns can be identified with a manageable level of noise.
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