A Comprehensive Study of Bloated Dependencies in the Maven Ecosystem
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Abstract (arXiv)
Build automation tools and package managers have a profound influence on software development. They facilitate the reuse of third-party libraries, support a clear separation between the application's code and its external dependencies, and automate several software development tasks. However, the wide adoption of these tools introduces new challenges related to dependency management. In this paper, we propose an original study of one such challenge: the emergence of bloated dependencies. Bloated dependencies are libraries that the build tool packages with the application's compiled code but that are actually not necessary to build and run the application. This phenomenon artificially grows the size of the built binary and increases maintenance effort. We propose a tool, called DepClean, to analyze the presence of bloated dependencies in Maven artifacts. We analyze 9,639 Java artifacts hosted on Maven Central, which include a total of 723,444 dependency relationships. Our key result is that 75.1% of the analyzed dependency relationships are bloated. In other words, it is feasible to reduce the number of dependencies of Maven artifacts up to 1/4 of its current count. We also perform a qualitative study with 30 notable open-source projects. Our results indicate that developers pay attention to their dependencies and are willing to remove bloated dependencies: 18/21 answered pull requests were accepted and merged by developers, removing 131 dependencies in total.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
5 papers.
- 2021Communicating Natural Programs to Humans and Machinescited by 35
- 2022Machine Learning Systems are Bloated and Vulnerablecited by 2
- 2025How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflowcited by 2
- 2024Predicting Likely-Vulnerable Code Changes: Machine Learning-based Vulnerability Protections for Android Open Source Projectcited by 0
- 2026Code That Works, Environments That Don't: Measuring Environment Reproducibility in AI-Generated Softwarecited by 0
- 2026Code That Works, Environments That Don't: Measuring Environment Reproducibility in AI-Generated Software30 Sep 2026
- 2025How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow14 Jul 2025
- 2024Predicting Likely-Vulnerable Code Changes: Machine Learning-based Vulnerability Protections for Android Open Source Project26 May 2024
- 2022Machine Learning Systems are Bloated and Vulnerable16 Dec 2022
- 2021Communicating Natural Programs to Humans and Machines15 Jun 2021