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AMRerank: A Framework for Library Migration Recommendations Using Multi‐Agent Analysis and Data‐Driven Reranking.

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Τίτλος: AMRerank: A Framework for Library Migration Recommendations Using Multi‐Agent Analysis and Data‐Driven Reranking.
Συγγραφείς: Luo, Jie1 (AUTHOR), Huang, Zijie2,3 (AUTHOR) huangzj@sscenter.sh.cn, Gao, Jianhua1 (AUTHOR), Sarwar, Nadeem (AUTHOR) nsarwar.bulc@bahria.edu.pk
Πηγή: IET Software (Wiley-Blackwell). 1/6/2026, Vol. 2026, p1-18. 18p.
Θεματικοί όροι: *Software libraries (Computer programming), *Software engineering, *Recommender systems, *Maintenance costs, *Data analysis, Inference (Logic)
Περίληψη: Open‐source libraries are indispensable for modern software development but can create substantial maintenance burdens when they become deprecated or unmaintained. Selecting an appropriate replacement among many candidates remains challenging, since methods relying only on historical mining or similarity metrics often miss subtle differences in meaning. We propose AMRerank, a novel framework that integrates multi‐agent qualitative analysis with a data‐driven, interpretable reranking model. AMRerank first deploys specialized agents to examine and classify semantic relationships between libraries, generating evidence‐backed labels and concise summaries. An interpretable reranking framework then fuses these qualitative signals with heuristic and semantic features to produce a fine‐grained, explainable ranking. Evaluated on the GT2014 benchmark against competitive baselines (LMG, MMR, MMRLC), AMRerank achieves Precision@1 of 0.899 and mean reciprocal rank (MRR) of 0.928. As our case studies show, the system provides actionable, human‐readable evidence that helps developers make more reliable migration choices. [ABSTRACT FROM AUTHOR]
Copyright of IET Software (Wiley-Blackwell) is the property of Wiley-Blackwell 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.)
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  Data: AMRerank: A Framework for Library Migration Recommendations Using Multi‐Agent Analysis and Data‐Driven Reranking.
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  Data: *<searchLink fieldCode="DE" term="%22Software+libraries+%28Computer+programming%29%22">Software libraries (Computer programming)</searchLink><br />*<searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink><br />*<searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Maintenance+costs%22">Maintenance costs</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Inference+%28Logic%29%22">Inference (Logic)</searchLink>
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  Data: Open‐source libraries are indispensable for modern software development but can create substantial maintenance burdens when they become deprecated or unmaintained. Selecting an appropriate replacement among many candidates remains challenging, since methods relying only on historical mining or similarity metrics often miss subtle differences in meaning. We propose AMRerank, a novel framework that integrates multi‐agent qualitative analysis with a data‐driven, interpretable reranking model. AMRerank first deploys specialized agents to examine and classify semantic relationships between libraries, generating evidence‐backed labels and concise summaries. An interpretable reranking framework then fuses these qualitative signals with heuristic and semantic features to produce a fine‐grained, explainable ranking. Evaluated on the GT2014 benchmark against competitive baselines (LMG, MMR, MMRLC), AMRerank achieves Precision@1 of 0.899 and mean reciprocal rank (MRR) of 0.928. As our case studies show, the system provides actionable, human‐readable evidence that helps developers make more reliable migration choices. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IET Software (Wiley-Blackwell) is the property of Wiley-Blackwell 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.)
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        Value: 10.1049/sfw2/2169889
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        Text: English
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      – SubjectFull: Inference (Logic)
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              Text: 1/6/2026
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