Uncovering Determinants of Code Quality in Education via Static Code Analysis
The role of static code analysis in enhancing the quality of software codes is widely acknowledged. Static code analysis facilitates the examination of code for irregularities without program execution, which significantly impacts project quality. Furthermore, tools for static code analysis serve as...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10591974/ |
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| author | Danilo Nikolic Darko Stefanovic Miroslav Nikolic Dusanka Dakic Miroslav Stefanovic Sara Koprivica |
| author_facet | Danilo Nikolic Darko Stefanovic Miroslav Nikolic Dusanka Dakic Miroslav Stefanovic Sara Koprivica |
| author_sort | Danilo Nikolic |
| collection | DOAJ |
| description | The role of static code analysis in enhancing the quality of software codes is widely acknowledged. Static code analysis facilitates the examination of code for irregularities without program execution, which significantly impacts project quality. Furthermore, tools for static code analysis serve as educational aids, imparting essential lessons on coding practices. Motivated by the growing complexity of software projects and the pivotal role of code quality in academic performance within computing disciplines, this research examines over 500 student projects using static code analysis tools. The aim is to determine metrics that influence the code quality of student projects. The study investigates how metrics, such as project setup, influence code quality and students’ academic performances. By adopting a broad approach, the investigation determines the overall impact of these metrics on the technical integrity of software engineering projects and academic outcomes. Insights derived from this study are anticipated to enhance teaching strategies and curriculum development, aiming to improve academic performance by promoting better code quality. |
| format | Article |
| id | doaj-art-21e641cd708b45edb9171fcf5efe97f8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-21e641cd708b45edb9171fcf5efe97f82024-11-19T00:03:31ZengIEEEIEEE Access2169-35362024-01-011216822916824410.1109/ACCESS.2024.342629910591974Uncovering Determinants of Code Quality in Education via Static Code AnalysisDanilo Nikolic0https://orcid.org/0000-0003-2084-3072Darko Stefanovic1https://orcid.org/0000-0001-9200-5092Miroslav Nikolic2Dusanka Dakic3https://orcid.org/0000-0002-1707-7616Miroslav Stefanovic4https://orcid.org/0000-0002-0767-365XSara Koprivica5https://orcid.org/0000-0001-7077-8780Faculty of Technical Sciences, University of Novi Sad, Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Novi Sad, SerbiaOpen Institute of Technology, University of Malta, Msida, MaltaFaculty of Technical Sciences, University of Novi Sad, Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Novi Sad, SerbiaThe role of static code analysis in enhancing the quality of software codes is widely acknowledged. Static code analysis facilitates the examination of code for irregularities without program execution, which significantly impacts project quality. Furthermore, tools for static code analysis serve as educational aids, imparting essential lessons on coding practices. Motivated by the growing complexity of software projects and the pivotal role of code quality in academic performance within computing disciplines, this research examines over 500 student projects using static code analysis tools. The aim is to determine metrics that influence the code quality of student projects. The study investigates how metrics, such as project setup, influence code quality and students’ academic performances. By adopting a broad approach, the investigation determines the overall impact of these metrics on the technical integrity of software engineering projects and academic outcomes. Insights derived from this study are anticipated to enhance teaching strategies and curriculum development, aiming to improve academic performance by promoting better code quality.https://ieeexplore.ieee.org/document/10591974/Academic performancecode qualityeducationeducational practicesstatic code analysis |
| spellingShingle | Danilo Nikolic Darko Stefanovic Miroslav Nikolic Dusanka Dakic Miroslav Stefanovic Sara Koprivica Uncovering Determinants of Code Quality in Education via Static Code Analysis IEEE Access Academic performance code quality education educational practices static code analysis |
| title | Uncovering Determinants of Code Quality in Education via Static Code Analysis |
| title_full | Uncovering Determinants of Code Quality in Education via Static Code Analysis |
| title_fullStr | Uncovering Determinants of Code Quality in Education via Static Code Analysis |
| title_full_unstemmed | Uncovering Determinants of Code Quality in Education via Static Code Analysis |
| title_short | Uncovering Determinants of Code Quality in Education via Static Code Analysis |
| title_sort | uncovering determinants of code quality in education via static code analysis |
| topic | Academic performance code quality education educational practices static code analysis |
| url | https://ieeexplore.ieee.org/document/10591974/ |
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