强化学习在需求工程中的应用:需求跟踪

Hakim Sultanov, J. Hayes
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引用次数: 43

摘要

我们假设机器学习可以有效地应用于解决需求工程问题。具体来说,我们提出了一种基于机器学习技术强化学习(RL)的需求追溯方法。RL方法演示了文本需求工件(例如,高级别需求跟踪到低级别需求)之间的候选链接的相当有针对性的生成。该技术已经使用来自两个问题域的两个真实数据集进行了验证。我们的技术比信息检索技术显示了统计上显著的更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of reinforcement learning to requirements engineering: requirements tracing
We posit that machine learning can be applied to effectively address requirements engineering problems. Specifically, we present a requirements traceability method based on the machine learning technique Reinforcement Learning (RL). The RL method demonstrates a rather targeted generation of candidate links between textual requirements artifacts (high level requirements traced to low level requirements, for example). The technique has been validated using two real-world datasets from two problem domains. Our technique demonstrated statistically significant better results than the Information Retrieval technique.
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