Towards Early Warning and Migration of High-Risk Dormant Open-Source Software Dependencies

Dormant open-source software (OSS) dependencies are no longer maintained or actively developed, their related code components are more vulnerable and error-prone since they can hardly keep up with evolving software dependents. Presently, their migration remains costly and challenging for practitioners. To tackle such a challenge, we intend to characterize, predict, and automatically migrate high-risk dormant OSS dependencies. Our pilot study of 4,945 Maven dependencies reveals over half of them are dormant, and 12.15% pose a high security risk. These high-risk dependencies can be predicted early based on their version release and usage characteristics. They are rarely migrated by developers, and simple one-to-one API migrations can be achieved with little context using Large Language Models (LLMs). Future research will be conducted on a more complete dataset, incorporate socio-technical features for improved high-risk prediction, and fine-tune a migration code generator.

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Publication Details

Published
2025-04-27
DOI
https://doi.org/10.1109/icse-nier66352.2025.00030
Citations
1
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
4.17

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article

Towards Early Warning and Migration of High-Risk Dormant Open-Source Software Dependencies

Lizhi Cai, Zijie Huang, Xuan Mao, Kang Yang
1 citations
Scientific Computing and Data Management
4.17
article

Towards Early Warning and Migration of High-Risk Dormant Open-Source Software Dependencies

Lizhi Cai, Zijie Huang, Xuan Mao, Kang Yang
article en
1 citations

Abstract

Dormant open-source software (OSS) dependencies are no longer maintained or actively developed, their related code components are more vulnerable and error-prone since they can hardly keep up with evolving software dependents. Presently, their migration remains costly and challenging for practitioners. To tackle such a challenge, we intend to characterize, predict, and automatically migrate high-risk dormant OSS dependencies. Our pilot study of 4,945 Maven dependencies reveals over half of them are dormant, and 12.15% pose a high security risk. These high-risk dependencies can be predicted early based on their version release and usage characteristics. They are rarely migrated by developers, and simple one-to-one API migrations can be achieved with little context using Large Language Models (LLMs). Future research will be conducted on a more complete dataset, incorporate socio-technical features for improved high-risk prediction, and fine-tune a migration code generator.

East China University of Science and Technology (CN)
China Postdoctoral Science Foundation, State Grid Hebei Energy Technology Service
Reduced inequalities
Openalex Percentile: Top 6%
Scientific Computing and Data Management
4.17
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