Evaluating Transformer-Based Embeddings for Software Change Recommendation: From General Models to Specialized Models

• Item type: SIG Technical Reports(1)
• 公開日: 2026-03-02
• タイトル / 言語: ja
• タイトル: Evaluating Transformer-Based Embeddings for Software Change Recommendation: From General Models to Specialized Models
• タイトル / 言語: en
• タイトル: Evaluating Transformer-Based Embeddings for Software Change Recommendation: From General Models to Specialized Models
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: School of Computing, Institute of Science Tokyo
• 著者所属: School of Computing, Institute of Science Tokyo
• 著者所属: School of Computing, Institute of Science Tokyo
• 著者所属(英): en
• 著者所属(英): School of Computing, Institute of Science Tokyo
• 著者所属(英): en
• 著者所属(英): School of Computing, Institute of Science Tokyo
• 著者所属(英): en
• 著者所属(英): School of Computing, Institute of Science Tokyo
• 著者名: Savira,Ramadhanty; Profir-petru,Pârţachi; Takashi,Kobayashi
• 著者名(英): Savira Ramadhanty; Profir-petru Pârţachi; Takashi Kobayashi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: As software evolves, dependencies between program elements become increasingly complex. This complexity often results in incomplete changes, leading to bugs due to the developers' inability to determine all impacted elements. To address this issue, previous work recommended co-change candidates at commit time. They do so based on a composite similarity over commits using textual information and changed items. However, when calculating the similarity of textual information, the semantics of code changes, which can serve as additional context, are not considered. Our proposed methods incorporate change semantics by deriving them from textual information using code-task pre-trained models. The goal is to allow the recommendation system to capture the overall context and characteristics of changes more accurately than existing methods.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: As software evolves, dependencies between program elements become increasingly complex. This complexity often results in incomplete changes, leading to bugs due to the developers' inability to determine all impacted elements. To address this issue, previous work recommended co-change candidates at commit time. They do so based on a composite similarity over commits using textual information and changed items. However, when calculating the similarity of textual information, the semantics of code changes, which can serve as additional context, are not considered. Our proposed methods incorporate change semantics by deriving them from textual information using code-task pre-trained models. The goal is to allow the recommendation system to capture the overall context and characteristics of changes more accurately than existing methods.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10112981
• 書誌情報: 研究報告ソフトウェア工学（SE）; 巻 2026-SE-222, 号 3, p. 1-6, 発行日 2026-03-02
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8825
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会