Learning I/O Variables from Scientific Software's User Manuals

被引:3
作者
Peng, Zedong [1 ]
Lin, Xuanyi [2 ]
Santhoshkumar, Sreelekhaa Nagamalli [1 ]
Niu, Nan [1 ]
Kanewala, Upulee [3 ]
机构
[1] Univ Cincinnati, Cincinnati, OH 45221 USA
[2] Oracle Amer Inc, Redwood Shores, CA 94065 USA
[3] Univ North Florida, Jacksonville, FL 32224 USA
来源
COMPUTATIONAL SCIENCE, ICCS 2022, PT IV | 2022年
关键词
Scientific software; User manual; Software documentation; Classification; Machine learning; THEORETICAL REPLICATION;
D O I
10.1007/978-3-031-08760-8_42
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Scientific software often involves many input and output variables. Identifying these variables is important for such software engineering tasks as metamorphic testing. To reduce the manual work, we report in this paper our investigation of machine learning algorithms in classifying variables from software's user manuals. We identify thirteen natural-language features, and use them to develop a multi-layer solution where the first layer distinguishes variables from non-variables and the second layer classifies the variables into input and output types. Our experimental results on three scientific software systems show that random forest and feedforward neural network can be used to best implement the first layer and second layer respectively.
引用
收藏
页码:503 / 516
页数:14
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