Comparison of Maintainability Index Measurement from Microsoft CodeLens and Line of Code

Akuwan Saleh
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引用次数: 1

Abstract

Higher software quality demands are in line with software quality assurance that can be implemented in every step of the software development process. Maintainability Index is a calculation used to review the level of maintenance of the software. MI has a close relationship with software quality parameters based on Halstead Volume (HV), Cyclomatic Complexity McCabe (CC), and Line of Code (LOC). MI calculations can be carried out automatically with the help of a framework that has been introduced in the industrial world, such as Microsoft Visual Studio 2015 in the form of Code Matric Analysis and an additional software named Microsoft CodeLens Code Health Indicator. Previous research explained the close relationships between LOC and HV, and LOC and CC. New equations can be acquired to calculate the MI with the LOC approach. The LOC Parameter is physically shaped in a software program so that the developer can understand it easily and quickly. The aim of this research is to automate the MI calculation process based on the component classification method of modules in a rule-based C # program file. These rules are based on the error of MI calculations that occur from the platform, and the estimation of MI with LOC classification rules generates an error rate of less than 20% (19.75 %) of the data, both of which have the same accuracy.
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微软CodeLens和代码行可维护性指标测量的比较
更高的软件质量要求与软件质量保证一致,可以在软件开发过程的每个步骤中实现。可维护性指数是一种用于审查软件维护水平的计算方法。MI与基于Halstead Volume (HV)、cyomatic Complexity McCabe (CC)和Line of Code (LOC)的软件质量参数有着密切的关系。MI计算可以在工业领域引入的框架的帮助下自动执行,例如以代码矩阵分析形式的Microsoft Visual Studio 2015和名为Microsoft CodeLens代码运行状况指示器的附加软件。以往的研究解释了LOC与HV、LOC与CC之间的密切关系,利用LOC方法可以获得新的计算MI的公式。LOC参数在软件程序中是物理形状的,因此开发人员可以轻松快速地理解它。本研究的目的是在基于规则的c#程序文件中,基于模块的组件分类方法实现MI计算过程的自动化。这些规则是基于平台上发生的MI计算误差,使用LOC分类规则估计MI产生的数据错误率小于20%(19.75%),两者具有相同的精度。
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