Advancing Blockchain-Oriented Software Engineering: Empirical Investigations and AI-Augmented Tooling for Secure Smart Contract Development

Abstract

Blockchain ecosystems are evolving rapidly, placing increasing demands on developers who build decentralized applications, including smart contracts—self-executing programs deployed on the blockchain that enforce agreements without intermediaries. Blockchain developers face immutable code, high-stakes systems, rapid cycles, and social media pressures—challenges often unmet by traditional software engineering. This dissertation investigates how blockchain developers work, how their behaviors are shaped by external signals—such as textit{crypto signals}, defined as public social media messages (e.g., tweets) from influential figures that affect market sentiment—and how generative AI tools can support secure smart contract development. Through a series of research studies, we: (1) surveyed blockchain developers to identify software engineering practices and pain points; (2) analyzed the influence of crypto signals on user activity and GitHub development patterns, and (3) evaluated the capabilities of large language models (LLMs), including ChatGPT, Gemini, and ChainGPT, for Solidity smart contract generation; and (4) evaluated smart contract security analyzers across two datasets, revealing substantial differences in vulnerability coverage, false-positive burden, execution robustness, and cross-dataset behavior. Building on these findings, this dissertation designs, implements, and evaluates textit{ChainSentinel}, an evidence-oriented framework for smart contract auditing. ChainSentinel integrates heterogeneous security analyzers within a provenance-preserving workflow that reconciles and prioritizes findings and supports selected executable validation and repair-oriented re-evaluation. Across the evaluated datasets, the framework substantially reduced the burden of unfiltered analyzer aggregation while continuing to produce audit reports under partial analyzer failure. Collectively, this work advances blockchain-oriented software engineering by connecting empirical understanding of blockchain development, generative AI, and security-analysis tools with the design of evidence-oriented techniques for more secure and transparent smart contract development and auditing.