Hybrid Classical-AI Systems for Software Testing and Bug Fixing

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Title: Hybrid Classical-AI Systems for Software Testing and Bug Fixing
Authors: Staron, Miroslaw, 1977, Abrahao, Silvia
Source: IEEE Software. 42(6):106-110
Description: Testing, debugging, and defect resolution remain difficult tasks in software engineering, often requiring significant time despite modern tools. This column reviews recent research on these topics, presented at the ACM FSE 2025 conference in Trondheim, highlighting new insights into improving bug detection and resolution.
Access URL: https://research.chalmers.se/publication/548946
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  Data: Hybrid Classical-AI Systems for Software Testing and Bug Fixing
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  Data: <searchLink fieldCode="AR" term="%22Staron%2C+Miroslaw%22">Staron, Miroslaw</searchLink>, 1977<br /><searchLink fieldCode="AR" term="%22Abrahao%2C+Silvia%22">Abrahao, Silvia</searchLink>
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  Data: <i>IEEE Software</i>. 42(6):106-110
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  Data: Testing, debugging, and defect resolution remain difficult tasks in software engineering, often requiring significant time despite modern tools. This column reviews recent research on these topics, presented at the ACM FSE 2025 conference in Trondheim, highlighting new insights into improving bug detection and resolution.
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        Value: 10.1109/MS.2025.3597698
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