Automatic Identification of Assumptions from the Hibernate Developer Mailing List

Ruiyin Li, Peng Liang, Chen Yang, Georgios Digkas, A. Chatzigeorgiou, Zhuang Xiong
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引用次数: 8

Abstract

During the software development life cycle, assumptions are an important type of software development knowledge that can be extracted from textual artifacts. Analyzing assumptions can help to, for example, comprehend software design and further facilitate software maintenance. Manual identification of assumptions by stakeholders is rather time-consuming, especially when analyzing a large dataset of textual artifacts. To address this problem, one promising way is to use automatic techniques for assumption identification. In this study, we conducted an experiment to evaluate the performance of existing machine learning classification algorithms for automatic assumption identification, through a dataset extracted from the Hibernate developer mailing list. The dataset is composed of 400 "Assumption" sentences and 400 "Non-Assumption" sentences. Seven classifiers using different machine learning algorithms were selected and evaluated. The experiment results show that the SVM algorithm achieved the best performance (with a precision of 0.829, a recall of 0.812, and an F1-score of 0.819). Additionally, according to the ROC curves and related AUC values, the SVM-based classifier comparatively performed better than other classifiers for the binary classification of assumptions.
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自动识别Hibernate开发者邮件列表中的假设
在软件开发生命周期中,假设是一种重要的软件开发知识类型,可以从文本工件中提取出来。例如,分析假设可以帮助理解软件设计并进一步促进软件维护。由涉众手动识别假设是相当耗时的,特别是在分析大量文本工件数据集时。为了解决这个问题,一个有希望的方法是使用自动假设识别技术。在这项研究中,我们通过从Hibernate开发者邮件列表中提取的数据集,进行了一项实验,以评估现有机器学习分类算法在自动假设识别方面的性能。该数据集由400个“假设”句子和400个“非假设”句子组成。选择并评估了使用不同机器学习算法的七个分类器。实验结果表明,SVM算法取得了最好的性能(准确率为0.829,召回率为0.812,f1得分为0.819)。此外,根据ROC曲线和相关AUC值,基于svm的分类器相对于其他分类器在假设二值分类方面表现更好。
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