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A Roadmap for Software Testing in Open Collaborative Development Environments

Qing Wang, Junjie Wang, Mingyang Li, Yawen Wang, Zhe Liu

TL;DR

This work surveys software quality assurance in open-collaborative and AI-powered development, framing the analysis around three orthogonal dimensions: Process, Personnel, and Technology. It synthesizes techniques from CI and test case prioritization to crowdtesting, tester recommendation, issue triaging, and human–AI collaboration, then surveys AI-driven testing methods, including LLM-based test generation, metamorphic testing, and testing for AI models. The paper highlights challenges such as information overload, the oracle problem in ML, and security risks in auto-generated code, while outlining opportunities like hybrid LLM-traditional approaches and human-in-the-loop paradigms. Altogether, it provides a roadmap for advancing software testing in open environments by integrating process improvements, human factors, and AI-powered technologies into a cohesive QA strategy.

Abstract

Amidst the ever-expanding digital sphere, the evolution of the Internet has not only fostered an atmosphere of information transparency and sharing but has also sparked a revolution in software development practices. The distributed nature of open collaborative development, along with its diverse contributors and rapid iterations, presents new challenges for ensuring software quality. This paper offers a comprehensive review and analysis of recent advancements in software quality assurance within open collaborative development environments. Our examination covers various aspects, including process management, personnel dynamics, and technological advancements, providing valuable insights into effective approaches for maintaining software quality in such collaborative settings. Furthermore, we delve into the challenges and opportunities arising from emerging technologies such as LLMs and the AI model-centric development paradigm. By addressing these topics, our study contributes to a deeper understanding of software quality assurance in open collaborative environments and lays the groundwork for future exploration and innovation.

A Roadmap for Software Testing in Open Collaborative Development Environments

TL;DR

This work surveys software quality assurance in open-collaborative and AI-powered development, framing the analysis around three orthogonal dimensions: Process, Personnel, and Technology. It synthesizes techniques from CI and test case prioritization to crowdtesting, tester recommendation, issue triaging, and human–AI collaboration, then surveys AI-driven testing methods, including LLM-based test generation, metamorphic testing, and testing for AI models. The paper highlights challenges such as information overload, the oracle problem in ML, and security risks in auto-generated code, while outlining opportunities like hybrid LLM-traditional approaches and human-in-the-loop paradigms. Altogether, it provides a roadmap for advancing software testing in open environments by integrating process improvements, human factors, and AI-powered technologies into a cohesive QA strategy.

Abstract

Amidst the ever-expanding digital sphere, the evolution of the Internet has not only fostered an atmosphere of information transparency and sharing but has also sparked a revolution in software development practices. The distributed nature of open collaborative development, along with its diverse contributors and rapid iterations, presents new challenges for ensuring software quality. This paper offers a comprehensive review and analysis of recent advancements in software quality assurance within open collaborative development environments. Our examination covers various aspects, including process management, personnel dynamics, and technological advancements, providing valuable insights into effective approaches for maintaining software quality in such collaborative settings. Furthermore, we delve into the challenges and opportunities arising from emerging technologies such as LLMs and the AI model-centric development paradigm. By addressing these topics, our study contributes to a deeper understanding of software quality assurance in open collaborative environments and lays the groundwork for future exploration and innovation.
Paper Structure (18 sections, 3 figures)

This paper contains 18 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Process Related
  3. Testing Process Management
  4. Testing Artifacts Management
  5. Personnel Related
  6. Tester Recommendation
  7. Issue Triaging
  8. Human–computer Collaborative Testing
  9. Technology Related
  10. Testing Benefited from Crowd Intelligence
  11. Testing with AI Models
  12. Testing for AI Models and Applications
  13. Challenges and Opportunities
  14. Leveraging LLMs for Enhancing Testing
  15. Testing for LLMs and LLM-centric Applications
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Overview about software testing in open-collaborative and AI-powered era
  • Figure 2: Distribution of testing tasks with LLMs wang2023software
  • Figure 3: Details of machine learning testing Zhang2022Machine