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Data Mining in Programs: Clustering Programs Based on Structure Metrics and Execution Values

Data Mining in Programs: Clustering Programs Based on Structure Metrics and Execution Values

TianTian Wang, KeChao Wang, XiaoHong Su, Lin Liu
Copyright: © 2020 |Volume: 16 |Issue: 2 |Pages: 16
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799804987|DOI: 10.4018/IJDWM.2020040104
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MLA

Wang, TianTian, et al. "Data Mining in Programs: Clustering Programs Based on Structure Metrics and Execution Values." IJDWM vol.16, no.2 2020: pp.48-63. http://doi.org/10.4018/IJDWM.2020040104

APA

Wang, T., Wang, K., Su, X., & Liu, L. (2020). Data Mining in Programs: Clustering Programs Based on Structure Metrics and Execution Values. International Journal of Data Warehousing and Mining (IJDWM), 16(2), 48-63. http://doi.org/10.4018/IJDWM.2020040104

Chicago

Wang, TianTian, et al. "Data Mining in Programs: Clustering Programs Based on Structure Metrics and Execution Values," International Journal of Data Warehousing and Mining (IJDWM) 16, no.2: 48-63. http://doi.org/10.4018/IJDWM.2020040104

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Abstract

Software exists in various control systems, such as security-critical systems and so on. Existing program clustering methods are limited in identifying functional equivalent programs with different syntactic representations. To solve this problem, firstly, a clustering method based on structured metric vectors was proposed to quickly identify structurally similar programs from a large number of existing programs. Next, a clustering method based on similar execution value sequences was proposed, to accurately identify the functional equivalent programs with code variations. This approach has been applied in automatic program repair, to identify sample programs from a large pool of template programs. The average purity value is 0.95576 and the average entropy is 0.15497. This means that the clustering partition is consistent with the expected partition.

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