Using Genetic Algorithm to Augment Test Data for Penalty Prediction

doi:10.23940/ijpe.20.07.p10.10781086

Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (7): 1078-1086.doi: 10.23940/ijpe.20.07.p10.10781086

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Using Genetic Algorithm to Augment Test Data for Penalty Prediction

Chunyan Xia^a,b, Xingya Wang^c,*, Yan Zhang^a, and Hao Yang^a

^aDepartment of Computer and Information Technology, Mudanjiang Normal University, Mudanjiang, 157011, China;
^bState Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China;
^cCollege of Computer Science and Technology, Nanjing Tech University, Nanjing, 210009, China

Submitted on ; Revised on ; Accepted on
Contact: * E-mail address: xingyawang@outlook.com
About author:Chunyan Xia is an associate professor of Department of Computer and Information Technology at Mudanjiang Normal University, her research interests include search-based software engineering, information processing and data mining. Xingya Wang is an associate professor of College of Computer Science and Technology at Nanjing Tech University, his research interests include blockchain analysis and test, software defect location. Yan Zhang is a professor of Department of Computer and Information Technology at Mudanjiang Normal University, her research interests include search-based software engineering. Hao Yang is an assistant of Department of Computer and Information Technology at Mudanjiang Normal University, his research interests include search-based software engineering.

Abstract

Abstract: With the development of smart court construction, a deep learning method has been introduced into the field of penalty prediction based on judicial text. Since the increasing parameters of the penalty prediction model, the size of the data set to test the performance of the model is gradually expanding. First, we use the data augmentation method to make some changes to the original data to obtain a large number of augmented data with the same label. Then, we use the multi-objective genetic algorithm to search for high-quality test data from a large number of augmented data, so as to improve the diversity of augmented data. Finally, we perform experiments. The results of actual judicial cases show that compared with the random method, augmented test data based on the genetic algorithm can better test the performance of the penalty prediction model.

Key words: penalty prediction, data augmentation, test data, genetic algorithm

Chunyan Xia, Xingya Wang, Yan Zhang, and Hao Yang. Using Genetic Algorithm to Augment Test Data for Penalty Prediction [J]. Int J Performability Eng, 2020, 16(7): 1078-1086.

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Using Genetic Algorithm to Augment Test Data for Penalty Prediction

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