Effort Estimation using Bayesian Networks for Agile Development

Claudio Ratke, Helcio Hoffmann, T. Gaspar, Pedro Edmundo Floriani
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引用次数: 5

Abstract

This article proposes an automatic method to estimate the effort of development based on narrative texts. Narrative used the agile method. We propose techniques of symbolic analysis of natural language were used for extraction of verbs and nouns, and verbal reduction (verbs in infinitive), and the standardization of keywords through synonyms. For the machine learning was used if the naive Bayesian classifier model. Apply and test the model in real environment that used the narratives in the Portuguese language in the form of BDD (Behavior Driven Development). In these tests, obtaining an accuracy of 83% in the estimates of the story points.
基于贝叶斯网络的敏捷开发工作量估算
本文提出了一种基于叙事性文本的开发工作量自动估算方法。叙事采用了敏捷方法。我们提出将自然语言的符号分析技术用于动词和名词的提取、动词不定式的动词还原以及通过同义词对关键词进行标准化。对于机器学习,如果使用朴素贝叶斯分类器模型。以BDD(行为驱动开发)的形式在真实环境中使用葡萄牙语的叙述,并对模型进行应用和测试。在这些测试中,在故事点的估计中获得83%的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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