Insights into Dependency Maintenance Trends in the Maven Ecosystem
- URL: http://arxiv.org/abs/2503.22902v1
- Date: Fri, 28 Mar 2025 22:20:24 GMT
- Title: Insights into Dependency Maintenance Trends in the Maven Ecosystem
- Authors: Barisha Chowdhury, Md Fazle Rabbi, S. M. Mahedy Hasan, Minhaz F. Zibran,
- Abstract summary: We present a quantitative analysis of the Neo4j dataset using the Goblin framework.<n>Our analysis reveals that releases with fewer dependencies have a higher number of missed releases.<n>Our study shows that the dependencies in the latest releases have positive freshness scores, indicating better software management efficacy.
- Score: 0.14999444543328289
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As modern software development increasingly relies on reusable libraries and components, managing dependencies has become critical for ensuring software stability and security. However, challenges such as outdated dependencies, missed releases, and the complexity of interdependent libraries can significantly impact project maintenance. In this paper, we present a quantitative analysis of the Neo4j dataset using the Goblin framework to uncover patterns of freshness in projects with different numbers of dependencies. Our analysis reveals that releases with fewer dependencies have a higher number of missed releases. Additionally, our study shows that the dependencies in the latest releases have positive freshness scores, indicating better software management efficacy. These results can encourage better management practices and contribute to the overall health of software ecosystems.
Related papers
- Towards Compatibly Mitigating Technical Lag in Maven Projects [5.833478907177207]
LagEase is a tool designed to address the challenges of mitigating the technical lags and avoid incompatibility risks and bloated dependencies.
Experimental results show that LagEase outperforms Maven Dependabot.
arXiv Detail & Related papers (2025-04-02T15:48:28Z) - Faster Releases, Fewer Risks: A Study on Maven Artifact Vulnerabilities and Lifecycle Management [0.14999444543328289]
We analyze the release histories of 10,000 Maven artifacts, covering over 203,000 releases and 1.7 million dependencies.<n>Our results show an inverse relationship between release speed and dependency outdatedness.<n>These findings emphasize the importance of accelerated release strategies in reducing security risks.
arXiv Detail & Related papers (2025-03-31T17:32:45Z) - Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute [61.00662702026523]
We propose a unified Test-Time Compute scaling framework that leverages increased inference-time instead of larger models.
Our framework incorporates two complementary strategies: internal TTC and external TTC.
We demonstrate our textbf32B model achieves a 46% issue resolution rate, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1.
arXiv Detail & Related papers (2025-03-31T07:31:32Z) - Tracking Down Software Cluster Bombs: A Current State Analysis of the Free/Libre and Open Source Software (FLOSS) Ecosystem [0.43981305860983705]
This study provides a summary of the current state of available FLOSS package repositories.<n>It addresses the challenge of identifying problematic areas within a software ecosystem.<n>The results indicate that while there are well-maintained projects within the FLOSS ecosystem, there are also high-impact projects that are susceptible to supply chain attacks.
arXiv Detail & Related papers (2025-02-12T08:57:57Z) - Pinning Is Futile: You Need More Than Local Dependency Versioning to Defend against Supply Chain Attacks [23.756533975349985]
Recent high-profile incidents in open-source software have raised practitioner attention on software supply chain attacks.<n>Security practitioners advocate pinning dependency to specific versions rather than floating in version ranges.<n>We quantify, through counterfactual analysis and simulations, the security and maintenance impact of version constraints in the npm ecosystem.
arXiv Detail & Related papers (2025-02-10T16:50:48Z) - An Overview and Catalogue of Dependency Challenges in Open Source Software Package Registries [52.23798016734889]
This article provides a catalogue of dependency-related challenges that come with relying on OSS packages or libraries.
The catalogue is based on the scientific literature on empirical research that has been conducted to understand, quantify and overcome these challenges.
arXiv Detail & Related papers (2024-09-27T16:20:20Z) - Agent-Driven Automatic Software Improvement [55.2480439325792]
This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs)
The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation.
We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
arXiv Detail & Related papers (2024-06-24T15:45:22Z) - A Preliminary Study on Self-Contained Libraries in the NPM Ecosystem [2.221643499902673]
The widespread of libraries within modern software ecosystems creates complex networks of dependencies.
One mitigation strategy involves reducing dependencies; libraries with zero dependencies become to self-contained.
This paper explores the characteristics of self-contained libraries within the NPM ecosystem.
arXiv Detail & Related papers (2024-06-17T09:33:49Z) - Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration [64.19431011897515]
This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue resolution.<n>Our approach introduces a top-down method to condense critical repository information into a knowledge graph, reducing complexity, and employs a Monte Carlo tree search based strategy.<n>In production deployment and evaluation at Alibaba Cloud, LingmaAgent automatically resolved 16.9% of in-house issues faced by development engineers, and solved 43.3% of problems after manual intervention.
arXiv Detail & Related papers (2024-06-03T15:20:06Z) - Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study [72.24266814625685]
We explore the performance of large language models (LLMs) across the entire software development lifecycle with DevEval.<n>DevEval features four programming languages, multiple domains, high-quality data collection, and carefully designed and verified metrics for each task.<n> Empirical studies show that current LLMs, including GPT-4, fail to solve the challenges presented within DevEval.
arXiv Detail & Related papers (2024-03-13T15:13:44Z) - Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models [81.27391252152199]
Large language models (LLMs) have achieved impressive performance across various natural language benchmarks.
We propose to automate dataset updating and provide systematic analysis regarding its effectiveness.
There are two updating strategies: 1) mimicking strategy to generate similar samples based on original data, and 2) extending strategy that further expands existing samples.
arXiv Detail & Related papers (2024-02-19T07:15:59Z) - Dependency Practices for Vulnerability Mitigation [4.710141711181836]
We analyze more than 450 vulnerabilities in the npm ecosystem to understand why dependent packages remain vulnerable.
We identify over 200,000 npm packages that are infected through their dependencies.
We use 9 features to build a prediction model that identifies packages that quickly adopt the vulnerability fix and prevent further propagation of vulnerabilities.
arXiv Detail & Related papers (2023-10-11T19:48:46Z) - Analyzing Maintenance Activities of Software Libraries [65.268245109828]
Industrial applications heavily integrate open-source software libraries nowadays.
I want to introduce an automatic monitoring approach for industrial applications to identify open-source dependencies that show negative signs regarding their current or future maintenance activities.
arXiv Detail & Related papers (2023-06-09T16:51:25Z) - SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks.
It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches.
We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
arXiv Detail & Related papers (2023-04-21T10:00:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.