Andrea Morichetta

Inter-organizational business processes describe the interaction between distributed parties. BPMN choreographies offer a modeling notation for business interactions between participants, setting aside their internal behaviors. In process mining, existing literature views choreographies through a single-case notion lens, attempting to fit interaction data involving several business entities into a flat event log format. In this paper, we investigate how the object-centric event log format OCEL~2.0 can be used to express choreography data. To that end, we present a mapping between the BPMN choreography metamodel and OCEL~2.0 elements. The mapping enforces BPMN choreography semantics by defining data constraints. We also present a process discovery technique for BPMN choreography diagrams that can handle participant bands, message elements, and process hierarchy.We demonstrate and evaluate the approach with 19 datasets from different domains. We provide implementations to extract choreography data, validate choreography event logs, and discover BPMN choreography diagrams.

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.

While decentralized applications adoption has surged in several domains, the smart contracts implementing them can diverge from their specified logic. To address this issue, the data generated during the execution of smart contracts can provide valuable insights into the actual behavior of an application, enabling its analysis. For this reason, we employ compliance checking to audit decentralized applications based on their execution data. To enable such a technique, we provide a threefold contribution. Firstly, we present a domain-specific language, called CoBlock, to define compliance rules by relying on blockchain characterizations as a first-class citizen. Then we designed a visual rule modeling solution, CoBlockly, which abstract the underlying language, to facilitate auditors on building blockchain-specific compliance rules. Finally, we implemented a tailored framework for blockchain compliance checking to streamline the process of defining and checking compliance rules over decentralized applications execution data. The evaluation, which employs the real-world CryptoKitties decentralized application, includes accuracy and performance analysis in addition to a usability assessment to measure the feasibility and to demonstrate the practicality of the framework.

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.