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arXiv 1708.04589cs.SE
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学习软件项目的有效变更

Learning Effective Changes for Software Projects

Rahul Krishna

摘要:

大部分软件分析的主要动机是决策。如何做出这些决定?一个人是否应该根据从一个特定项目中得出的经验教训来做决定?还是应该从多个项目中产生这些决策?本文试图回答这些问题。我们的工作是由一个认识到,目前的软件分析工具主要集中在预测。事实上,预测是一项有用的任务,但它通常是关于需要采取什么行动的“计划”。这项研究旨在通过寻求支持可操作分析的方法来解决规划任务,这些分析提供了明确的指导。具体来说,我们提出了XTREE和BELLTREE算法,用于在项目内和项目间生成一组可操作的计划。如果遵循这些计划中的每一个,将提高软件项目的质量。

英文摘要:

The primary motivation of much of software analytics is decision making. How to make these decisions? Should one make decisions based on lessons that arise from within a particular project? Or should one generate these decisions from across multiple projects? This work is an attempt to answer these questions. Our work was motivated by a realization that much of the current generation software analytics tools focus primarily on prediction. Indeed prediction is a useful task, but it is usually followed by "planning" about what actions need to be taken. This research seeks to address the planning task by seeking methods that support actionable analytics that offer clear guidance on what to do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set of actionable plans within and across projects. Each of these plans, if followed will improve the quality of the software project.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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