Morena Barboni

The immutability of deployed smart contracts increases the impact of undetected faults. Mutation testing evaluates test suite effectiveness by injecting controlled faults (mutants) into contract code and observing whether existing tests detect them. However, the number of generated mutants and the required test executions make mutation testing costly, limiting its scalability in realistic settings. We present ASCENT , an online test prioritization technique based on a Neural Monte Carlo Tree Search (Neural-MCTS) algorithm that dynamically learns and adapts test execution strategies during mutation analysis. The approach prioritizes tests in real time without requiring prior knowledge of the system under test, reducing execution cost while preserving fault detection effectiveness. This paper provides a comprehensive evaluation across five real-world Solidity projects. We analyze behavior under a wide range of hyperparameter configurations, examine how different state representations influence prioritization performance, and evaluate an asynchronous execution model that decouples search, inference, and training to reduce wall-clock execution time. The results show consistent reductions across all projects, ranging from 28–61% depending on project features and hyperparameter configurations, alongside a parallelized variant that substantially reduces wall-clock execution time with controlled trade-offs in prioritization performance.

Blockchain technology is increasingly adopted in scenarios requiring trust and data integrity. On the Ethereum blockchain, the proxy pattern has become increasingly popular because it allows smart contract code to evolve while preserving stored data. However, a key challenge remains ensuring that such upgrades do not introduce breaking changes or cause disruptions to other contracts and off-chain systems. In this article, we introduce Catana , a framework that leverages historical transactions for Capture-Replay testing of proxy-based Upgradeable Smart Contracts (USCs). Catana assesses the potential impact of an upgrade by comparing the outcomes of replayed transactions with those from the previous version deployed on the main network. Additionally, it extracts and decodes contract state variables, providing deeper insights into how code changes affect the contract state, and helping developers mitigate issues before deployment. Experiments demonstrate that analyzing storage data accounts for the majority (about 86.5%) of detected disruptive upgrades. We also evaluate different policies for building replay test suites from historical transactions. Results identify a strategy that maximizes effectiveness while requiring a small number of replay test executions. Even a test suite containing just one transaction per each invoked method achieved good effectiveness (about 60%) in detecting disruptive upgrades.