Comparison of Maintainability Index Measurement from Microsoft CodeLens and Line of Code
| Autor: | Akuwan Saleh, R. Rizki Rachmadi, Elsa Mayang Sari, Haryadi Amran Darwito, Gilang Heru Kencana |
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| Rok vydání: | 2020 |
| Předmět: |
Programming language
Computer science business.industry Maintainability 020207 software engineering Cyclomatic complexity 02 engineering and technology computer.software_genre Microsoft Visual Studio Software quality Software development process Software Software quality assurance 020204 information systems 0202 electrical engineering electronic engineering information engineering Halstead complexity measures business computer |
| Zdroj: | 2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI). |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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