A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection
<p dir="ltr">The increasing complexity of software development demands efficient automated bug report priority classification, and recent advancements in deep learning hold promise. This paper presents a comparative study of contemporary learning paradigms, including BERT, vector dat...
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2024
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| _version_ | 1864513543591690240 |
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| author | Eyüp Halit Yilmaz (21400700) |
| author2 | İsmail Hakki Toroslu (21400703) Ömer Köksal (21400706) |
| author2_role | author author |
| author_facet | Eyüp Halit Yilmaz (21400700) İsmail Hakki Toroslu (21400703) Ömer Köksal (21400706) |
| author_role | author |
| dc.creator.none.fl_str_mv | Eyüp Halit Yilmaz (21400700) İsmail Hakki Toroslu (21400703) Ömer Köksal (21400706) |
| dc.date.none.fl_str_mv | 2024-08-28T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ACCESS.2024.3451125 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Comparative_Study_of_Contemporary_Learning_Paradigms_in_Bug_Report_Priority_Detection/29605112 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Applied computing Artificial intelligence Data management and data science Machine learning Software engineering Bug triaging Contrastive learning Machine learning Natural language processing Software bug report classification Software engineering |
| dc.title.none.fl_str_mv | A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The increasing complexity of software development demands efficient automated bug report priority classification, and recent advancements in deep learning hold promise. This paper presents a comparative study of contemporary learning paradigms, including BERT, vector databases, large language models (LLMs), and a simple novel learning paradigm, contrastive learning for BERT. Utilizing datasets from bug reports, movie reviews, and app reviews, we evaluate and compare the performance of each approach. We find that transformer encoder-only models outperform in classification tasks measured by the precision, recall, and F1 score transformer decoder-only models despite an order of magnitude gap between the number of parameters. The novel use of contrastive learning for BERT demonstrates promising results in capturing subtle nuances in text data. This work highlights the potential of advanced NLP techniques for automated bug report priority classification and underscores the importance of considering multiple factors when developing models for this task. The paper’s main contributions are a comprehensive evaluation of various learning paradigms, such as vector databases and LLMs, an introduction of contrastive learning for BERT, an exploration of applicability to other text classification tasks, and a contrastive learning procedure that exploits ordinal information between classes.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3451125" target="_blank">https://dx.doi.org/10.1109/access.2024.3451125</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_554d68e988cea3758224f90bc5c42688 |
| identifier_str_mv | 10.1109/ACCESS.2024.3451125 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29605112 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority DetectionEyüp Halit Yilmaz (21400700)İsmail Hakki Toroslu (21400703)Ömer Köksal (21400706)Information and computing sciencesApplied computingArtificial intelligenceData management and data scienceMachine learningSoftware engineeringBug triagingContrastive learningMachine learningNatural language processingSoftware bug report classificationSoftware engineering<p dir="ltr">The increasing complexity of software development demands efficient automated bug report priority classification, and recent advancements in deep learning hold promise. This paper presents a comparative study of contemporary learning paradigms, including BERT, vector databases, large language models (LLMs), and a simple novel learning paradigm, contrastive learning for BERT. Utilizing datasets from bug reports, movie reviews, and app reviews, we evaluate and compare the performance of each approach. We find that transformer encoder-only models outperform in classification tasks measured by the precision, recall, and F1 score transformer decoder-only models despite an order of magnitude gap between the number of parameters. The novel use of contrastive learning for BERT demonstrates promising results in capturing subtle nuances in text data. This work highlights the potential of advanced NLP techniques for automated bug report priority classification and underscores the importance of considering multiple factors when developing models for this task. The paper’s main contributions are a comprehensive evaluation of various learning paradigms, such as vector databases and LLMs, an introduction of contrastive learning for BERT, an exploration of applicability to other text classification tasks, and a contrastive learning procedure that exploits ordinal information between classes.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3451125" target="_blank">https://dx.doi.org/10.1109/access.2024.3451125</a></p>2024-08-28T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ACCESS.2024.3451125https://figshare.com/articles/journal_contribution/A_Comparative_Study_of_Contemporary_Learning_Paradigms_in_Bug_Report_Priority_Detection/29605112CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296051122024-08-28T06:00:00Z |
| spellingShingle | A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection Eyüp Halit Yilmaz (21400700) Information and computing sciences Applied computing Artificial intelligence Data management and data science Machine learning Software engineering Bug triaging Contrastive learning Machine learning Natural language processing Software bug report classification Software engineering |
| status_str | publishedVersion |
| title | A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection |
| title_full | A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection |
| title_fullStr | A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection |
| title_full_unstemmed | A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection |
| title_short | A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection |
| title_sort | A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection |
| topic | Information and computing sciences Applied computing Artificial intelligence Data management and data science Machine learning Software engineering Bug triaging Contrastive learning Machine learning Natural language processing Software bug report classification Software engineering |