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.

翻译：背景：区块链与人工智能技术正日益被探索用于增强软件工程领域的可信性，特别是在支持软件演化任务方面。方法：我们采用预先定义且包含明确纳入标准的规程，开展了一项系统性文献综述，以确保研究的透明度、可重复性并最小化偏差，系统梳理了关于区块链技术如何为人工智能驱动的软件工程工具与流程建立可信性的相关研究。结果：多数研究聚焦于人工智能在软件工程中的集成应用，仅31%的研究明确探讨了可信性问题。本综述重点分析了六项近期研究，这些研究探索了基于区块链的方法，旨在强化人工智能辅助软件工程任务中的可靠性、透明度和问责性。结论：区块链通过确保数据不可篡改性、模型透明度和生命周期问责制（包括结合区块链共识的联邦学习及私有数据验证）来增强可信性。然而，对"可信性"定义的不一致以及有限的现实世界测试仍是主要挑战。未来的工作必须开发可度量、可复现的可信性框架，以构建可靠、安全且合规的人工智能驱动软件工程生态系统，包括涉及大语言模型的应用。