OpenLambdaVerse: A Dataset and Analysis of Open-Source Serverless Applications

2025-08-02

AI Summary

This study characterizes real-world serverless application practices built on the Serverless Framework and AWS Lambda. Addressing limitations of existing datasets—including narrow coverage, poor timeliness, and high noise—we propose Wonderless, an enhanced methodology integrating multi-stage filtering with Infrastructure-as-Code (IaC)-driven static analysis. The latter encompasses YAML configuration parsing, GitHub repository crawling, and code-level feature extraction. Using this approach, we construct OpenLambdaVerse: a high-precision, up-to-date dataset of open-source serverless projects. To our knowledge, OpenLambdaVerse is the first to systematically reveal empirical distributions and evolutionary trends across key dimensions—including application scale and complexity, dominant programming languages and runtimes, event-triggering patterns, project maturity, and security practices. The dataset serves as an authoritative benchmark for architectural research, tool development, and industrial serverless engineering.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

Function-as-a-Service (FaaS) is at the core of serverless computing, enabling developers to easily deploy applications without managing computing resources. With an Infrastructure-as-Code (IaC) approach, frameworks like the Serverless Framework use YAML configurations to define and deploy APIs, tasks, workflows, and event-driven applications on cloud providers, promoting zero-friction development. As with any rapidly evolving ecosystem, there is a need for updated insights into how these tools are used in real-world projects. Building on the methodology established by the Wonderless dataset for serverless computing (and applying multiple new filtering steps), OpenLambdaVerse addresses this gap by creating a dataset of current GitHub repositories that use the Serverless Framework in applications that contain one or more AWS Lambda functions. We then analyze and characterize this dataset to get an understanding of the state-of-the-art in serverless architectures based on this stack. Through this analysis we gain important insights on the size and complexity of current applications, which languages and runtimes they employ, how are the functions triggered, the maturity of the projects, and their security practices (or lack of). OpenLambdaVerse thus offers a valuable, up-to-date resource for both practitioners and researchers that seek to better understand evolving serverless workloads.

Problem

• Analyzing real-world usage of Serverless Framework in GitHub repositories
• Understanding current trends in serverless architectures and AWS Lambda
• Evaluating application size, complexity, languages, triggers, and security practices

Innovation

• Uses Serverless Framework for FaaS deployment
• Applies IaC with YAML configurations
• Analyzes GitHub repositories for AWS Lambda