AI-Assisted Smart Contract Testing and Validation Frameworks

Abstract

Smart contract testing today is a manual craft. Developers write unit tests by hand, choose which edge cases to cover based on experience and intuition, and hope they have not missed the one input sequence that drains the protocol. Coverage tools tell them which lines executed but not which attack scenarios were explored. Fuzzers generate random inputs but without understanding of the contract's economic logic. AI-assisted testing changes this equation by using machine learning to generate test cases that target high-risk code regions, to synthesise property specifications from contract behaviour, and to predict which untested paths are most likely to harbour bugs. I present the AI-Assisted Smart Contract Testing Framework (AASCTF), evaluating four AI-assisted testing strategies -- LLM-generated test suites, reinforcement-learning-guided fuzzing, neural property synthesis, and coverage-predictive test prioritisation -- on 60 real DeFi contracts with known vulnerability status. The Testing Effectiveness Score (TES) measures branch coverage, vulnerability detection rate, test generation speed, and developer adoption friction. Key finding: RL-guided fuzzing achieves the highest TES (0.912) by learning which input sequences reach deep contract states that random fuzzing rarely explores, increasing branch coverage from 62.4% (random Echidna) to 89.8% and vulnerability detection from 68.4% to 91.2%.