论文标题
软件开发中的机器学习和价值产生：调查
Machine Learning and value generation in Software Development: a survey
论文作者
论文摘要
机器学习（ML）已成为预测和分类数据的普遍工具，并在包括软件开发（SD）在内的多个问题域中找到了应用程序。本文回顾了2000年至2019年之间的文献，内容涉及用于编程工作估算的学习模型，预测风险以及识别和检测缺陷。这项工作旨在为愿意将ML添加到其软件开发工具箱中的从业人员的起点。它对最近的文献进行了分类，并确定了趋势和局限性。调查显示，一些作者同意，ML对SD的工业应用并不像报告结果所表明的那样受欢迎。进行的调查表明，尽管对各种SD任务的发现有希望的发现，但大多数研究都会产生模糊的结果，部分原因是该问题域中缺乏全面的数据集。本文以结论性的评论和未来研究的建议结尾。
Machine Learning (ML) has become a ubiquitous tool for predicting and classifying data and has found application in several problem domains, including Software Development (SD). This paper reviews the literature between 2000 and 2019 on the use the learning models that have been employed for programming effort estimation, predicting risks and identifying and detecting defects. This work is meant to serve as a starting point for practitioners willing to add ML to their software development toolbox. It categorises recent literature and identifies trends and limitations. The survey shows as some authors have agreed that industrial applications of ML for SD have not been as popular as the reported results would suggest. The conducted investigation shows that, despite having promising findings for a variety of SD tasks, most of the studies yield vague results, in part due to the lack of comprehensive datasets in this problem domain. The paper ends with concluding remarks and suggestions for future research.
