Original scientific paper

https://doi.org/10.17559/TV-20211029115834

An Experimental Study on Attribute Validity of Code Quality Evaluation Model

Tianze Guo ; State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China No. 10, Xitucheng Road, Haidian District, Beijing
Hanli Bai* ; Institute of Computational Aerodynamics, China Aerodynamics Research and Development Center, No.6, South Section,Second Ring Road, Mianyang City, Sichuan Province,P.R. China
Yunzhan Gong ; State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China No. 10, Xitucheng Road, Haidian District, Beijing
Yawen Wang ; 1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China No. 10, Xitucheng Road, Haidian District, Beijing 2. Guangxi Key Laboratory of Cryptography and Information Security, Guilin, Guangxi 541004, China
Dahai Jin ; State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China No. 10, Xitucheng Road, Haidian District, Beijing

Abstract

Regarding the practicality of the quality evaluation model, the lack of quantitative experimental evaluation affects the effective use of the quality model, and also a lack of effective guidance for choosing the model. Aiming at this problem, based on the sensitivity of the quality evaluation model to code defects, a machine learning-based quality evaluation attribute validity verification method is proposed. This method conducts comparative experiments by controlling variables. First, extract the basic metric elements; then, convert them into quality attributes of the software; finally, to verify the quality evaluation model and the effectiveness of medium quality attributes, this paper compares machine learning methods based on quality attributes with those based on text features, and conducts experimental evaluation in two data sets. The result shows that the effectiveness of quality attributes under control variables is better, and leads by 15% in AdaBoostClassifier; when the text feature extraction method is increased to 50 - 150 dimensions, the performance of the text feature in the four machine learning algorithms overtakes the quality attributes; but when the peak is reached, quality attributes are more stable. This also provides a direction for the optimization of the quality model and the use of quality assessment in different situations.

Keywords

Code metrics; Feature extraction; Machine learning; Quality evaluation

Hrčak ID:

272598

URI

https://hrcak.srce.hr/272598

Publication date:

15.4.2022.

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