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| Autori principali: | , , , |
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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2505.17977 |
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| _version_ | 1866912389677449216 |
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| author | Daneshyan, Farbod He, Runzhi Wu, Jianyu Zhou, Minghui |
| author_facet | Daneshyan, Farbod He, Runzhi Wu, Jianyu Zhou, Minghui |
| contents | The release note is a crucial document outlining changes in new software versions. Yet, many developers view the process of writing software release notes as a tedious and dreadful task. Consequently, numerous tools have been developed by researchers and practitioners to automate the generation of software release notes. However, these tools fail to consider project domain and target audience for personalisation, limiting their relevance and conciseness. Additionally, they suffer from limited applicability, often necessitating significant workflow adjustments and adoption efforts, hindering practical use and stressing developers. Despite recent advancements in natural language processing and the proven capabilities of large language models in various code and text-related tasks, there are no existing studies investigating the integration and utilisation of LLMs in automated release note generation. Therefore, we propose SmartNote, a novel and widely applicable release note generation approach that produces high-quality, contextually personalised release notes using LLM technology. SmartNote aggregates changes and uses an LLM to describe and summarise the changes using code, commit, and pull request details. It categorises and scores commits to generate structured and concise release notes of prioritised changes. Our human and automatic evaluations reveal that SmartNote outperforms or achieves comparable performance to DeepRelease, Conventional Changelog, and the projects'original release notes across four quality metrics: completeness, clarity, conciseness, and organisation. In both evaluations, SmartNote ranked first for completeness and organisation, while clarity ranked first in the human evaluation. A further evaluation demonstrates that SmartNote is effective in terms of context awareness and applicability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17977 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SmartNote: An LLM-Powered, Personalised Release Note Generator That Just Works Daneshyan, Farbod He, Runzhi Wu, Jianyu Zhou, Minghui Software Engineering The release note is a crucial document outlining changes in new software versions. Yet, many developers view the process of writing software release notes as a tedious and dreadful task. Consequently, numerous tools have been developed by researchers and practitioners to automate the generation of software release notes. However, these tools fail to consider project domain and target audience for personalisation, limiting their relevance and conciseness. Additionally, they suffer from limited applicability, often necessitating significant workflow adjustments and adoption efforts, hindering practical use and stressing developers. Despite recent advancements in natural language processing and the proven capabilities of large language models in various code and text-related tasks, there are no existing studies investigating the integration and utilisation of LLMs in automated release note generation. Therefore, we propose SmartNote, a novel and widely applicable release note generation approach that produces high-quality, contextually personalised release notes using LLM technology. SmartNote aggregates changes and uses an LLM to describe and summarise the changes using code, commit, and pull request details. It categorises and scores commits to generate structured and concise release notes of prioritised changes. Our human and automatic evaluations reveal that SmartNote outperforms or achieves comparable performance to DeepRelease, Conventional Changelog, and the projects'original release notes across four quality metrics: completeness, clarity, conciseness, and organisation. In both evaluations, SmartNote ranked first for completeness and organisation, while clarity ranked first in the human evaluation. A further evaluation demonstrates that SmartNote is effective in terms of context awareness and applicability. |
| title | SmartNote: An LLM-Powered, Personalised Release Note Generator That Just Works |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2505.17977 |