基于Mikado方法的软件代码膨胀和安全识别模型:重构实践

IF 0.7 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Jordan Journal of Electrical Engineering Pub Date : 2023-01-01 DOI:10.5455/jjee.204-1667422472
T. Gandomani, Hamid Sichani, Behzad Soleimani Neysiani
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引用次数: 0

摘要

术语“代码气味”或“糟糕气味”指的是编写错误的代码,反映了软件设计中的严重缺陷。一些代码气味会导致软件代码中的安全漏洞。到目前为止,这些代码的识别主要是通过软件工具完成的,而不是通过过程方法或模型。基于Mikado方法,提出了一种使用语法-度量解析器引擎检测不安全软件代码膨胀和安全漏洞的模型。这个名为Touba的模型对发现的案例进行评估和分析,并为代码审查和统计分析提供了一种交互式方法。采用所提出的模型对Juliet测试套件进行测试,表明其在选择的精度、召回率和f测量方面表现出色。结果表明,与现有工具相比,该模型的准确率提高了20.3%,召回率提高了16.76%,F-measure平均提高了18.61%。这些结果表明所提出的安全漏洞识别模型是本研究的主要贡献。
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Software Code Bloats and Security Identification Model Based on Mikado Methodology: a Refactoring Practice
The term “code smell” or “bad smell” refers to a code that has been written incorrectly and reflects severe defects in software design. Some code smells cause, particularly, security vulnerabilities in software codes. Until now, identification of these codes is mainly done through software tools and not by process methods or models. Based on the Mikado methodology, this paper proposes a model that uses a syntax-metric parser engine to detect insecure software code bloats and security vulnerabilities. This model, named Touba, assesses and analyzes the discovered cases and provides an interactive method for code review and statistical analysis. Employing the proposed model in testing the Juliet Test Suites shows its outstanding performance in terms of the selected measures of precision, recall, and F-measure. The obtained results show that the proposed model has a better performance - compared to the existing tools - in terms of accuracy by 20.3%, recall by 16.76%, and F-measure by 18.61% on average. These results indicate the effectiveness of the proposed - security vulnerability identification - model as the main contribution of this investigation.
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