Exploring the ChatGPT Approach for Bidirectional Traceability Problem between Design Models and Code
This study explores the capabilities of Large Language Models, particularly OpenAI's ChatGPT, in addressing the challenges associated with software modeling, explicitly focusing on the bidirectional traceability problem between design models and code. The objective of this study is to demonstra...
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| creator | Kanuka, Hideyuki Koreki, Genta Soga, Ryo Nishikawa, Kazu |
| description | This study explores the capabilities of Large Language Models, particularly
OpenAI's ChatGPT, in addressing the challenges associated with software
modeling, explicitly focusing on the bidirectional traceability problem between
design models and code. The objective of this study is to demonstrate the
proficiency of ChatGPT in understanding and integrating specific requirements
into design models and code. We also explore its potential to offer solutions
to the bidirectional traceability problem through a case study. The findings
indicate that ChatGPT is capable of generating design models and code from
natural language requirements, thereby bridging the gap between these
requirements and software modeling. Despite its limitations in suggesting a
specific method to resolve the problem using ChatGPT itself, it exhibited the
capacity to provide corrections to be consistent between design models and
code. As a result, the study concludes that achieving bidirectional
traceability between design models and code is feasible using ChatGPT. |
| doi_str_mv | 10.48550/arxiv.2309.14992 |
| format | Article |
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OpenAI's ChatGPT, in addressing the challenges associated with software
modeling, explicitly focusing on the bidirectional traceability problem between
design models and code. The objective of this study is to demonstrate the
proficiency of ChatGPT in understanding and integrating specific requirements
into design models and code. We also explore its potential to offer solutions
to the bidirectional traceability problem through a case study. The findings
indicate that ChatGPT is capable of generating design models and code from
natural language requirements, thereby bridging the gap between these
requirements and software modeling. Despite its limitations in suggesting a
specific method to resolve the problem using ChatGPT itself, it exhibited the
capacity to provide corrections to be consistent between design models and
code. As a result, the study concludes that achieving bidirectional
traceability between design models and code is feasible using ChatGPT.</description><identifier>DOI: 10.48550/arxiv.2309.14992</identifier><language>eng</language><subject>Computer Science - Software Engineering</subject><creationdate>2023-09</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2309.14992$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2309.14992$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Kanuka, Hideyuki</creatorcontrib><creatorcontrib>Koreki, Genta</creatorcontrib><creatorcontrib>Soga, Ryo</creatorcontrib><creatorcontrib>Nishikawa, Kazu</creatorcontrib><title>Exploring the ChatGPT Approach for Bidirectional Traceability Problem between Design Models and Code</title><description>This study explores the capabilities of Large Language Models, particularly
OpenAI's ChatGPT, in addressing the challenges associated with software
modeling, explicitly focusing on the bidirectional traceability problem between
design models and code. The objective of this study is to demonstrate the
proficiency of ChatGPT in understanding and integrating specific requirements
into design models and code. We also explore its potential to offer solutions
to the bidirectional traceability problem through a case study. The findings
indicate that ChatGPT is capable of generating design models and code from
natural language requirements, thereby bridging the gap between these
requirements and software modeling. Despite its limitations in suggesting a
specific method to resolve the problem using ChatGPT itself, it exhibited the
capacity to provide corrections to be consistent between design models and
code. As a result, the study concludes that achieving bidirectional
traceability between design models and code is feasible using ChatGPT.</description><subject>Computer Science - Software Engineering</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8FOwzAQRH3hgAofwIn9gYTYjp34WEIpSEX0kHu0iTeNJTeO3AjavycUNIcZaUYjPcYeeJbmpVLZE8az-0qFzEzKc2PELbOb8-RDdOMB5oGgGnDe7mtYT1MM2A3QhwjPzrpI3ezCiB7qiB1h67ybL7CPofV0hJbmb6IRXujkDiN8BEv-BDhaqJZ4x2569Ce6__cVq183dfWW7D6379V6l6AuRFLwVi_ipix1n5tW29xqnpVU5FqqpSAhhRayy7kqUOm-08uSo-kpUyVXcsUe_26vmM0U3RHjpfnFba648gfPCE9X</recordid><startdate>20230926</startdate><enddate>20230926</enddate><creator>Kanuka, Hideyuki</creator><creator>Koreki, Genta</creator><creator>Soga, Ryo</creator><creator>Nishikawa, Kazu</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20230926</creationdate><title>Exploring the ChatGPT Approach for Bidirectional Traceability Problem between Design Models and Code</title><author>Kanuka, Hideyuki ; Koreki, Genta ; Soga, Ryo ; Nishikawa, Kazu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a672-71b6b6b19886f49b6d4d6108e746356b1e232623c4157a56fc68861a9fe058153</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Software Engineering</topic><toplevel>online_resources</toplevel><creatorcontrib>Kanuka, Hideyuki</creatorcontrib><creatorcontrib>Koreki, Genta</creatorcontrib><creatorcontrib>Soga, Ryo</creatorcontrib><creatorcontrib>Nishikawa, Kazu</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kanuka, Hideyuki</au><au>Koreki, Genta</au><au>Soga, Ryo</au><au>Nishikawa, Kazu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exploring the ChatGPT Approach for Bidirectional Traceability Problem between Design Models and Code</atitle><date>2023-09-26</date><risdate>2023</risdate><abstract>This study explores the capabilities of Large Language Models, particularly
OpenAI's ChatGPT, in addressing the challenges associated with software
modeling, explicitly focusing on the bidirectional traceability problem between
design models and code. The objective of this study is to demonstrate the
proficiency of ChatGPT in understanding and integrating specific requirements
into design models and code. We also explore its potential to offer solutions
to the bidirectional traceability problem through a case study. The findings
indicate that ChatGPT is capable of generating design models and code from
natural language requirements, thereby bridging the gap between these
requirements and software modeling. Despite its limitations in suggesting a
specific method to resolve the problem using ChatGPT itself, it exhibited the
capacity to provide corrections to be consistent between design models and
code. As a result, the study concludes that achieving bidirectional
traceability between design models and code is feasible using ChatGPT.</abstract><doi>10.48550/arxiv.2309.14992</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Software Engineering |
| title | Exploring the ChatGPT Approach for Bidirectional Traceability Problem between Design Models and Code |
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