Investigating Technical Debt Types, Issues, and Solutions in Serverless Computing

2026-03-23

AI Summary

This study addresses the lack of systematic research on technical debt in serverless computing. Leveraging 78,867 relevant questions from Stack Overflow, it presents the first approach that integrates deep learning with qualitative analysis to automatically identify and annotate technical debt manifestations. The findings reveal that 37% of the examined posts involve technical debt, and the study identifies six serverless-specific types of technical debt, elucidating their characteristic patterns and corresponding mitigation strategies. By providing empirical evidence and a structured characterization of technical debt in this emerging paradigm, this work fills a critical gap in the literature and offers a theoretical foundation and practical insights for developing detection tools and governance practices in serverless environments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Responsible Web: Sustainability and climate impact of web technologies
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Serverless computing is a cloud execution model where developers run code, and the server management is handled by the cloud provider. Serverless computing is increasingly gaining popularity as more systems adopt it to enhance scalability and reduce operational costs. While it has numerous benefits, it also embodies unique challenges inherent to serverless computing. One such challenge is Technical Debt (TD), which is exacerbated by the complexities of the serverless paradigm. While prior work has investigated the activities and bad practices that lead to TD in serverless computing, there remains a gap in understanding how TD manifests, the challenges it poses, and the solutions proposed to address TD issues in serverless systems. This study aims to investigate TD in the serverless context using Stack Overflow (SO) as a knowledge base. We collected 78,867 serverless questions on SO and labeled them as TD or non-TD using deep learning. Moreover, we conducted an in-depth analysis to identify types of TD in serverless settings, associated issues, and proposed solutions. We found that 37% of the serverless questions on SO are TD-related. We also identified six serverless-specific issues. Our research highlights the need for tools that can effectively detect TD in serverless applications.

Problem

• Technical Debt
• Serverless Computing
• Software Challenges
• Cloud Computing
• Debt Manifestation

Innovation

• Technical Debt
• Serverless Computing
• Deep Learning
• Stack Overflow
• Software Maintenance