Towards Compatibly Mitigating Technical Lag in Maven Projects
April 02, 2025 Β· Declared Dead Β· π 2025 IEEE/ACM 47th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Rui Lu
arXiv ID
2504.01843
Category
cs.SE: Software Engineering
Citations
0
Venue
2025 IEEE/ACM 47th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
Last Checked
4 months ago
Abstract
Library reuse is a widely adopted practice in software development, however, re-used libraries are not always up-to-date, thus including unnecessary bugs or vulnerabilities. Brutely upgrading libraries to the latest versions is not feasible because breaking changes and bloated dependencies could be introduced, which may break the software project or introduce maintenance efforts. Therefore, balancing the technical lag reduction and the prevention of newly introduced issues are critical for dependency management. To this end, LagEase is introduced as a novel tool designed to address the challenges of mitigating the technical lags and avoid incompatibility risks and bloated dependencies. Experimental results show that LagEase outperforms Dependabot, providing a more effective solution for managing Maven dependencies.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted