Evaluating Developers’ Expertise in Serverless Functions by Mining Activities from Multiple Platforms
Abstract-- In the domain of software development, the evaluation of developer expertise has gained prominence, particularly with the rise of serverless functions. These functions, which simplify the development process by delegating infrastructure management to cloud providers, are becoming more com...
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| Format: | Article |
| Language: | English |
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Ferdowsi University of Mashhad
2024-12-01
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| Series: | Computer and Knowledge Engineering |
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| Online Access: | https://cke.um.ac.ir/article_45050_36105fcfe5a32214e46e243087fae958.pdf |
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| author | Aref Talebzadeh Bardsiri Abbas Rasoolzadegan |
| author_facet | Aref Talebzadeh Bardsiri Abbas Rasoolzadegan |
| author_sort | Aref Talebzadeh Bardsiri |
| collection | DOAJ |
| description | Abstract-- In the domain of software development, the evaluation of developer expertise has gained prominence, particularly with the rise of serverless functions. These functions, which simplify the development process by delegating infrastructure management to cloud providers, are becoming more common. As developers may utilize functions created by their peers, understanding the expertise of the original developer is crucial since it can serve as an indicator of the functions' quality. While there are existing methods for expertise evaluation, certain gaps remain, especially concerning serverless functions. To address this, our research aims to enhance the assessment of developer expertise in this area by extracting activity-based features from both GitHub and Stack Overflow. After processing the extracted data, we applied various machine learning algorithms. Our findings suggest a potential improvement in evaluating developer expertise when incorporating features from Stack Overflow compared to using only GitHub data. The extent of this improvement was observed to differ among programming languages, with variations in accuracy improvement percentages ranging from 2% to 19%. This study contributes to the ongoing discourse on developer expertise evaluation, highlighting the potential benefits of drawing from multiple data sources. |
| format | Article |
| id | doaj-art-d46ea711a7c543029e88de835988ac10 |
| institution | Kabale University |
| issn | 2538-5453 2717-4123 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ferdowsi University of Mashhad |
| record_format | Article |
| series | Computer and Knowledge Engineering |
| spelling | doaj-art-d46ea711a7c543029e88de835988ac102025-01-19T04:04:23ZengFerdowsi University of MashhadComputer and Knowledge Engineering2538-54532717-41232024-12-0172274210.22067/cke.2024.84447.110345050Evaluating Developers’ Expertise in Serverless Functions by Mining Activities from Multiple PlatformsAref Talebzadeh Bardsiri0Abbas Rasoolzadegan1Department of Computer Engineering, Ferdowsi University of Mashhad, Iran.Department of Computer Engineering, Ferdowsi University of Mashhad, Iran.Abstract-- In the domain of software development, the evaluation of developer expertise has gained prominence, particularly with the rise of serverless functions. These functions, which simplify the development process by delegating infrastructure management to cloud providers, are becoming more common. As developers may utilize functions created by their peers, understanding the expertise of the original developer is crucial since it can serve as an indicator of the functions' quality. While there are existing methods for expertise evaluation, certain gaps remain, especially concerning serverless functions. To address this, our research aims to enhance the assessment of developer expertise in this area by extracting activity-based features from both GitHub and Stack Overflow. After processing the extracted data, we applied various machine learning algorithms. Our findings suggest a potential improvement in evaluating developer expertise when incorporating features from Stack Overflow compared to using only GitHub data. The extent of this improvement was observed to differ among programming languages, with variations in accuracy improvement percentages ranging from 2% to 19%. This study contributes to the ongoing discourse on developer expertise evaluation, highlighting the potential benefits of drawing from multiple data sources.https://cke.um.ac.ir/article_45050_36105fcfe5a32214e46e243087fae958.pdfkeywords-- developer expertise evaluationdata analysismachine learning algorithmsserverless functionssoftware development |
| spellingShingle | Aref Talebzadeh Bardsiri Abbas Rasoolzadegan Evaluating Developers’ Expertise in Serverless Functions by Mining Activities from Multiple Platforms Computer and Knowledge Engineering keywords-- developer expertise evaluation data analysis machine learning algorithms serverless functions software development |
| title | Evaluating Developers’ Expertise in Serverless Functions by Mining Activities from Multiple Platforms |
| title_full | Evaluating Developers’ Expertise in Serverless Functions by Mining Activities from Multiple Platforms |
| title_fullStr | Evaluating Developers’ Expertise in Serverless Functions by Mining Activities from Multiple Platforms |
| title_full_unstemmed | Evaluating Developers’ Expertise in Serverless Functions by Mining Activities from Multiple Platforms |
| title_short | Evaluating Developers’ Expertise in Serverless Functions by Mining Activities from Multiple Platforms |
| title_sort | evaluating developers expertise in serverless functions by mining activities from multiple platforms |
| topic | keywords-- developer expertise evaluation data analysis machine learning algorithms serverless functions software development |
| url | https://cke.um.ac.ir/article_45050_36105fcfe5a32214e46e243087fae958.pdf |
| work_keys_str_mv | AT areftalebzadehbardsiri evaluatingdevelopersexpertiseinserverlessfunctionsbyminingactivitiesfrommultipleplatforms AT abbasrasoolzadegan evaluatingdevelopersexpertiseinserverlessfunctionsbyminingactivitiesfrommultipleplatforms |