Classification of Testable and Valuable User Stories by using Supervised Machine Learning Classifiers

Isha Subedi, Maninder Singh, Vijayalakshmi Ramasamy, G. Walia
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引用次数: 1

Abstract

Agile is one of the most widely used software development methodologies that include user stories, the smallest units semi-structured specifications to capture the requirements from a user's point of view. Despite being popular, only a little research has been done to automate the quality checking/analysis of a user story before assigning it to a sprint. In this study, we have chosen two metrics, i.e., Testable and Valuable criteria from INVEST checklist, and have applied supervised machine learning classifiers to automatically classify them. Since the industrial data collected for the research was unbalanced, we also applied data balancing techniques such as SMOTE, RUS, ROS, and Back translation (BT) to verify if they improved any classification metrics. Although we did not see any significant improvements in accuracy and precision for the classifiers after applying data balancing techniques, we noticed a significant improvement in recall values across all the classifiers. Our research provides some promising insights into how this research could be used in the software industry to automate the analysis of user stories and improve the quality of software produced.
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使用监督机器学习分类器对可测试和有价值的用户故事进行分类
敏捷是最广泛使用的软件开发方法之一,它包括用户故事,从用户的角度捕获需求的最小单元半结构化规范。尽管很流行,但在将用户故事分配到sprint之前,对其进行自动化质量检查/分析的研究很少。在本研究中,我们从INVEST检查表中选择了两个指标,即可测试和有价值的标准,并应用监督机器学习分类器对它们进行自动分类。由于为研究收集的工业数据是不平衡的,我们还应用了数据平衡技术,如SMOTE、RUS、ROS和Back translation (BT),以验证它们是否改进了任何分类指标。虽然在应用数据平衡技术后,我们没有看到分类器的准确度和精度有任何显著的提高,但我们注意到所有分类器的召回值都有显著的提高。我们的研究提供了一些有希望的见解,说明如何在软件行业中使用该研究来自动分析用户故事并提高所生成软件的质量。
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