Exploring the Landscape of Programming Language Identification With Machine Learning Approaches
The increasing complexity of modern software development necessitates tools and methodologies for code analysis, maintenance, and migration in multi-language Integrated Development Environments (IDEs). The security needs of the software development process also recently led to the introduction of th...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10870104/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859641943064576 |
|---|---|
| author | Amandeep Verma Rahul Saha Gulshan Kumar Alessandro Brighente Mauro Conti Tai-Hoon Kim |
| author_facet | Amandeep Verma Rahul Saha Gulshan Kumar Alessandro Brighente Mauro Conti Tai-Hoon Kim |
| author_sort | Amandeep Verma |
| collection | DOAJ |
| description | The increasing complexity of modern software development necessitates tools and methodologies for code analysis, maintenance, and migration in multi-language Integrated Development Environments (IDEs). The security needs of the software development process also recently led to the introduction of the Software Bill of Materials (SBOM), which vendors must include in their products. Automated tools become imperative to speed up software production and distribution. In the present code development landscape, automated Programming Language Identification (PLI) helps to develop secure software development with its in-depth insights into programming consequences for code maintenance, legacy system management, code analysis, quality assurance, software modernization, migration, and code search. With the increasing success of Machine Learning (ML) models in detection methods, researchers use them for PLI showing superior performance of detection compared to the basic method of identification based on the source file extension. In this paper, we present the first survey for ML-based PLI methods providing insights into the domain’s present status and guiding towards a futuristic tool development for PLI. We evaluate various ML techniques that recognize programming languages. These techniques include conventional, operation-based, and data source-based techniques. Our study examines the advantages and limitations of identifying the programming language in text, images, and videos. Our investigation also emphasizes the capabilities of the existing solutions that improve software development practices. Furthermore, our survey analyzes the strengths and limitations of existing code detection tools such as GitHub’s Linguist, Pygments, highlight.js, Ace, Google’s Code Prettify, SourcererCC, Guesslang, and SonarLint. We also provide some open research problems for future researchers. |
| format | Article |
| id | doaj-art-c8a22a73be584a0db1270554e524b775 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c8a22a73be584a0db1270554e524b7752025-02-11T00:00:59ZengIEEEIEEE Access2169-35362025-01-0113235562357910.1109/ACCESS.2025.353810810870104Exploring the Landscape of Programming Language Identification With Machine Learning ApproachesAmandeep Verma0Rahul Saha1https://orcid.org/0000-0003-3921-9512Gulshan Kumar2https://orcid.org/0000-0003-0026-149XAlessandro Brighente3https://orcid.org/0000-0001-6138-2995Mauro Conti4https://orcid.org/0000-0002-3612-1934Tai-Hoon Kim5https://orcid.org/0000-0003-0117-8102School of Computer Applications, Lovely Professional University, Punjab, IndiaDepartment of Mathematics, University of Padua, Padua, ItalyDepartment of Mathematics, University of Padua, Padua, ItalyDepartment of Mathematics, University of Padua, Padua, ItalyDepartment of Mathematics, University of Padua, Padua, ItalySchool of Electrical and Computer Engineering, Chonnam National University, Gwangju, Republic of KoreaThe increasing complexity of modern software development necessitates tools and methodologies for code analysis, maintenance, and migration in multi-language Integrated Development Environments (IDEs). The security needs of the software development process also recently led to the introduction of the Software Bill of Materials (SBOM), which vendors must include in their products. Automated tools become imperative to speed up software production and distribution. In the present code development landscape, automated Programming Language Identification (PLI) helps to develop secure software development with its in-depth insights into programming consequences for code maintenance, legacy system management, code analysis, quality assurance, software modernization, migration, and code search. With the increasing success of Machine Learning (ML) models in detection methods, researchers use them for PLI showing superior performance of detection compared to the basic method of identification based on the source file extension. In this paper, we present the first survey for ML-based PLI methods providing insights into the domain’s present status and guiding towards a futuristic tool development for PLI. We evaluate various ML techniques that recognize programming languages. These techniques include conventional, operation-based, and data source-based techniques. Our study examines the advantages and limitations of identifying the programming language in text, images, and videos. Our investigation also emphasizes the capabilities of the existing solutions that improve software development practices. Furthermore, our survey analyzes the strengths and limitations of existing code detection tools such as GitHub’s Linguist, Pygments, highlight.js, Ace, Google’s Code Prettify, SourcererCC, Guesslang, and SonarLint. We also provide some open research problems for future researchers.https://ieeexplore.ieee.org/document/10870104/Programming languagedetectionmachine learningcodesoftware |
| spellingShingle | Amandeep Verma Rahul Saha Gulshan Kumar Alessandro Brighente Mauro Conti Tai-Hoon Kim Exploring the Landscape of Programming Language Identification With Machine Learning Approaches IEEE Access Programming language detection machine learning code software |
| title | Exploring the Landscape of Programming Language Identification With Machine Learning Approaches |
| title_full | Exploring the Landscape of Programming Language Identification With Machine Learning Approaches |
| title_fullStr | Exploring the Landscape of Programming Language Identification With Machine Learning Approaches |
| title_full_unstemmed | Exploring the Landscape of Programming Language Identification With Machine Learning Approaches |
| title_short | Exploring the Landscape of Programming Language Identification With Machine Learning Approaches |
| title_sort | exploring the landscape of programming language identification with machine learning approaches |
| topic | Programming language detection machine learning code software |
| url | https://ieeexplore.ieee.org/document/10870104/ |
| work_keys_str_mv | AT amandeepverma exploringthelandscapeofprogramminglanguageidentificationwithmachinelearningapproaches AT rahulsaha exploringthelandscapeofprogramminglanguageidentificationwithmachinelearningapproaches AT gulshankumar exploringthelandscapeofprogramminglanguageidentificationwithmachinelearningapproaches AT alessandrobrighente exploringthelandscapeofprogramminglanguageidentificationwithmachinelearningapproaches AT mauroconti exploringthelandscapeofprogramminglanguageidentificationwithmachinelearningapproaches AT taihoonkim exploringthelandscapeofprogramminglanguageidentificationwithmachinelearningapproaches |