Infusion of Blockchain to Establish Trustworthiness in AI Supported Software Evolution: A Systematic Literature Review

TL;DR

This systematic review examines how blockchain technology can enhance trustworthiness in AI-supported software evolution by ensuring data integrity, transparency, and accountability, highlighting recent approaches and ongoing challenges.

Contribution

The paper provides a comprehensive synthesis of blockchain-based methods to improve trust in AI-driven software engineering, identifying gaps and future research directions.

Findings

Most studies focus on AI integration in SE, with only 31% addressing trustworthiness.

Six recent studies explore blockchain for reliability, transparency, and accountability.

Blockchain enhances trust through data immutability, model transparency, and lifecycle management.

Abstract

Context: Blockchain and AI are increasingly explored to enhance trustworthiness in software engineering (SE), particularly in supporting software evolution tasks. Method: We conducted a systematic literature review (SLR) using a predefined protocol with clear eligibility criteria to ensure transparency, reproducibility, and minimized bias, synthesizing research on blockchain-enabled trust in AI-driven SE tools and processes. Results: Most studies focus on integrating AI in SE, with only 31% explicitly addressing trustworthiness. Our review highlights six recent studies exploring blockchain-based approaches to reinforce reliability, transparency, and accountability in AI-assisted SE tasks. Conclusion: Blockchain enhances trust by ensuring data immutability, model transparency, and lifecycle accountability, including federated learning with blockchain consensus and private data verification. However, inconsistent definitions of trust and limited real-world testing remain major challenges. Future work must develop measurable, reproducible trust frameworks to enable reliable, secure, and compliant AI-driven SE ecosystems, including applications involving large language models.