Mutation testing is a widely recognized technique for assessing and enhancing the effectiveness of software test suites by introducing deliberate code mutations. However, its application often results in overly large test suites, as developers generate numerous tests to kill specific mutants, increasing computational overhead. This paper introduces PRIMG (Prioritization and Refinement Integrated Mutation-driven Generation), a novel framework for incremental and adaptive test case generation for Solidity smart contracts. PRIMG integrates two core components: a mutation prioritization module, which employs a machine learning model trained on mutant subsumption graphs to predict the usefulness of surviving mutants, and a test case generation module, which utilizes Large Language Models (LLMs) to generate and iteratively refine test cases to achieve syntactic and behavioral correctness.

Blockchain has revolutionized finance through decentralization, eliminating the need for traditional intermediaries. However, security concerns remain a major barrier to adoption, as DeFi platforms increasingly face targeted attacks. In this paper, we present the first methodology for automatically assessing and quantifying the risk of fund loss in DeFi projects due to smart contract exploits. By analyzing on-chain behaviors that signal potential malicious interactions, our approach assigns a dynamic risk score to DeFi projects over time. Relying solely on on-chain data ensures resistance to data manipulation and enhances the integrity of the assessment.We evaluated 220 compromised and 200 unaffected DeFi projects on multiple EVM-compatible blockchains – including Ethereum, BSC, Polygon, Arbitrum, Optimism, and Fantom – and conducted a comparative risk assessment on these projects. Our findings reveal statistically significant differences in risk scores before attacks compared to a control group without attacks. We anticipated potential threats to 86% of the projects that were later attacked, one day before the incidents, with a precision of 78%.

Smart contracts hold the potential to revolutionize various industries, but their implementation requires thorough testing due to the associated financial risks. Mutation testing is a powerful technique that can boost the fault-detection capabilities of a test suite, but it can also foster a deeper understanding of smart contract behavior. This work investigates the productivity of mutants with respect to their capabilities in disclosing Solidity issues. Based on these findings, it proposes an enhanced mutation strategy to better assist smart contract auditors during code inspection activities. 9 novel mutation operators are introduced in this paper and 13 existing operators are improved. The results show a $30 \%$ reduction in the number of generated mutants and time savings of $62 \%$, while increasing the set of productive mutants related to issues by $43 \%$ overall. We note that the most valuable type of mutants that could help disclose an issue as a result of manual mutant inspection was increased by $125 \%$.

Multi-Objective Combinatorial Optimization (MOCO) is fundamental to the development and optimization of software systems. We propose five novel parallel algorithms for solving MOCO problems exactly and efficiently. Our algorithms rely on off-the-shelf solvers to search for exact Pareto-optimal solutions, and they parallelize the search via collaborative communication, divide-and-conquer, or both. We demonstrate the feasibility and performance of our algorithms by experiments on three case studies of software-system designs. A key finding is that one algorithm, which we call FS-GIA, achieves substantial (even super-linear) speedups that scale well up to 64 cores. Furthermore, we analyze the performance bottlenecks and opportunities of our parallel algorithms, which facilitates further research on exact, parallel MOCO.