Automated Test-Case Generation for Solidity Smart Contracts: the AGSolT Approach and its Evaluation

Damian A. Tamburri; Dario Di Nucci; Geert Monsieur; Stefan Driessen; Willem-Jan van den Heuvel

Automated Test-Case Generation for Solidity Smart Contracts: the AGSolT Approach and its Evaluation

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arxiv 2102.08864 v4 pith:XKI6CND6 submitted 2021-02-17 cs.SE

Stefan Driessen , Dario Di Nucci , Geert Monsieur , Damian A. Tamburri , Willem-Jan van den Heuvel This is my paper · ORCID

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keywords smartcontractsagsoltsoliditytestsearchsomesuites

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Blockchain and smart contract technology are novel approaches to data and code management that facilitate trusted computing by allowing for development in a distributed and decentralized manner. Testing smart contracts comes with its own set of challenges which have not yet been fully identified and explored. Although existing tools can identify and discover known vulnerabilities and their interactions on the Ethereum blockchain through random search or symbolic execution, these tools generally do not produce test suites suitable for human oracles. In this paper, we present AGSOLT (Automated Generator of Solidity Test Suites). We demonstrate its efficiency by implementing two search algorithms to automatically generate test suites for stand-alone Solidity smart contracts, taking into account some of the blockchain-specific challenges. To test AGSOLT, we compared a random search algorithm and a genetic algorithm on a set of 36 real-world smart contracts. We found that AGSOLT is capable of achieving high branch coverage with both approaches and even discovered some errors in some of the most popular Solidity smart contracts on Github.

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PRIMG : Efficient LLM-driven Test Generation Using Mutant Prioritization
cs.SE 2025-05 reject novelty 4.0 of 10

PRIMG combines mutant prioritization with LLM-based iterative test refinement, and its evaluation on three Solidity projects suggests prioritized mutant targeting yields more killed mutants than random targeting.

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