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：区块链与人工智能正日益被探索用于增强软件工程（SE）中的可信性，特别是在支持软件演化任务方面。方法：我们采用预定义协议与明确的纳入标准进行了系统性文献综述（SLR），以确保透明度、可重复性并最小化偏差，综合分析了区块链在人工智能驱动的SE工具与流程中实现可信性的相关研究。结果：大多数研究聚焦于人工智能在SE中的集成，仅31%的研究明确探讨可信性问题。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以增强人工智能辅助SE任务中的可靠性、透明度与可问责性。结论：区块链通过确保数据不可篡改性、模型透明度及生命周期可问责性（包括结合区块链共识的联邦学习与私有数据验证）来提升可信性。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SE生态系统，包括涉及大语言模型的应用。