Academic Journal
Empowering Agile-Based Generative Software Development through Human-AI Teamwork.
| Τίτλος: | Empowering Agile-Based Generative Software Development through Human-AI Teamwork. |
|---|---|
| Συγγραφείς: | Zhang, Sai, Xing, Zhenchang, Guo, Ronghui, Xu, Fangzhou, Chen, Lei, Zhang, Zhaoyuan, Zhang, Xiaowang, Feng, Zhiyong, Zhuang, Zhiqiang |
| Πηγή: | ACM Transactions on Software Engineering & Methodology; Jul2025, Vol. 34 Issue 6, p1-46, 46p |
| Θεματικοί όροι: | Agile software development, Computer software development, Groupware (Computer software), Program generators (Computer programs), Information needs, Quality standards |
| Περίληψη: | In software development, the raw requirements proposed by users are frequently incomplete, which impedes the complete implementation of software functionalities. With the emergence of large language models, the exploration of generating software through user requirements has attracted attention. Recent methods with the top-down waterfall model employ a questioning approach for requirement completion, attempting to explore further user requirements. However, users, constrained by their domain knowledge, result in a lack of effective acceptance criteria during the requirement completion, failing to fully capture the implicit needs of the user. Moreover, the cumulative errors of the waterfall model can lead to discrepancies between the generated code and user requirements. The Agile methodologies reduce cumulative errors of the waterfall model through lightweight iteration and collaboration with users, but the challenge lies in ensuring semantic consistency between user requirements and the code generated by the agent. To address these challenges, we propose AgileGen, an agile-based generative software development through human-AI teamwork. Unlike existing questioning agents, AgileGen adopts a novel collaborative approach that breaks free from the constraints of domain knowledge by initiating the end-user perspective to complete the acceptance criteria. By introducing the Gherkin language, AgileGen attempts for the first time to use testable requirement descriptions as a bridge for semantic consistency between requirements and code, aiming to ensure that software products meet actual user requirements by defining user scenarios that include acceptance criteria. Additionally, we innovate in the human-AI teamwork model, allowing users to participate in decision-making processes they do well and significantly enhancing the completeness of software functionality. To ensure semantic consistency between requirements and generated code, we derive consistency factors from Gherkin to drive the subsequent software code generation. Finally, to improve the reliability of user scenarios, we also introduce a memory pool mechanism, collecting user decision-making scenarios and recommending them to new users with similar requirements. AgileGen, as a user-friendly interactive system, significantly outperformed existing best methods by 16.4% and garnered higher user satisfaction. [ABSTRACT FROM AUTHOR] |
| Copyright of ACM Transactions on Software Engineering & Methodology is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=1049331X&ISBN=&volume=34&issue=6&date=20250701&spage=1&pages=1-46&title=ACM Transactions on Software Engineering & Methodology&atitle=Empowering%20Agile-Based%20Generative%20Software%20Development%20through%20Human-AI%20Teamwork.&aulast=Zhang%2C%20Sai&id=DOI:10.1145/3702987 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 186887327 RelevancyScore: 1007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1007.33386230469 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Empowering Agile-Based Generative Software Development through Human-AI Teamwork. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Sai%22">Zhang, Sai</searchLink><br /><searchLink fieldCode="AR" term="%22Xing%2C+Zhenchang%22">Xing, Zhenchang</searchLink><br /><searchLink fieldCode="AR" term="%22Guo%2C+Ronghui%22">Guo, Ronghui</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Fangzhou%22">Xu, Fangzhou</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Lei%22">Chen, Lei</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zhaoyuan%22">Zhang, Zhaoyuan</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaowang%22">Zhang, Xiaowang</searchLink><br /><searchLink fieldCode="AR" term="%22Feng%2C+Zhiyong%22">Feng, Zhiyong</searchLink><br /><searchLink fieldCode="AR" term="%22Zhuang%2C+Zhiqiang%22">Zhuang, Zhiqiang</searchLink> – Name: TitleSource Label: Source Group: Src Data: ACM Transactions on Software Engineering & Methodology; Jul2025, Vol. 34 Issue 6, p1-46, 46p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Agile+software+development%22">Agile software development</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Groupware+%28Computer+software%29%22">Groupware (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22Program+generators+%28Computer+programs%29%22">Program generators (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Information+needs%22">Information needs</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+standards%22">Quality standards</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In software development, the raw requirements proposed by users are frequently incomplete, which impedes the complete implementation of software functionalities. With the emergence of large language models, the exploration of generating software through user requirements has attracted attention. Recent methods with the top-down waterfall model employ a questioning approach for requirement completion, attempting to explore further user requirements. However, users, constrained by their domain knowledge, result in a lack of effective acceptance criteria during the requirement completion, failing to fully capture the implicit needs of the user. Moreover, the cumulative errors of the waterfall model can lead to discrepancies between the generated code and user requirements. The Agile methodologies reduce cumulative errors of the waterfall model through lightweight iteration and collaboration with users, but the challenge lies in ensuring semantic consistency between user requirements and the code generated by the agent. To address these challenges, we propose AgileGen, an agile-based generative software development through human-AI teamwork. Unlike existing questioning agents, AgileGen adopts a novel collaborative approach that breaks free from the constraints of domain knowledge by initiating the end-user perspective to complete the acceptance criteria. By introducing the Gherkin language, AgileGen attempts for the first time to use testable requirement descriptions as a bridge for semantic consistency between requirements and code, aiming to ensure that software products meet actual user requirements by defining user scenarios that include acceptance criteria. Additionally, we innovate in the human-AI teamwork model, allowing users to participate in decision-making processes they do well and significantly enhancing the completeness of software functionality. To ensure semantic consistency between requirements and generated code, we derive consistency factors from Gherkin to drive the subsequent software code generation. Finally, to improve the reliability of user scenarios, we also introduce a memory pool mechanism, collecting user decision-making scenarios and recommending them to new users with similar requirements. AgileGen, as a user-friendly interactive system, significantly outperformed existing best methods by 16.4% and garnered higher user satisfaction. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of ACM Transactions on Software Engineering & Methodology is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=186887327 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1145/3702987 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 46 StartPage: 1 Subjects: – SubjectFull: Agile software development Type: general – SubjectFull: Computer software development Type: general – SubjectFull: Groupware (Computer software) Type: general – SubjectFull: Program generators (Computer programs) Type: general – SubjectFull: Information needs Type: general – SubjectFull: Quality standards Type: general Titles: – TitleFull: Empowering Agile-Based Generative Software Development through Human-AI Teamwork. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Sai – PersonEntity: Name: NameFull: Xing, Zhenchang – PersonEntity: Name: NameFull: Guo, Ronghui – PersonEntity: Name: NameFull: Xu, Fangzhou – PersonEntity: Name: NameFull: Chen, Lei – PersonEntity: Name: NameFull: Zhang, Zhaoyuan – PersonEntity: Name: NameFull: Zhang, Xiaowang – PersonEntity: Name: NameFull: Feng, Zhiyong – PersonEntity: Name: NameFull: Zhuang, Zhiqiang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1049331X Numbering: – Type: volume Value: 34 – Type: issue Value: 6 Titles: – TitleFull: ACM Transactions on Software Engineering & Methodology Type: main |
| ResultId | 1 |