Advancing Test Suite Reduction And Smart Contract Verification: A Comprehensive Analysis Of Techniques, Quality Metrics, And Fuzzing Approaches

Authors

  • Johnathan Mercer Department of Computer Science, University of Edinburgh, Edinburgh, UK

DOI:

https://doi.org/10.37547/

Keywords:

Test Suite Reduction, Smart Contract Verification, Fuzzing Techniques

Abstract

The rapid evolution of software systems and distributed applications has magnified the necessity for efficient testing and robust quality assurance. Test suite reduction (TSR) and smart contract verification have emerged as critical components in ensuring software reliability, performance, and security. This research systematically examines contemporary TSR methodologies, their effectiveness, and quality assessment criteria while exploring advances in smart contract fuzzing and design patterns to enhance transactional integrity. Drawing on extensive empirical and theoretical studies, this paper identifies key gaps in current practices, critically evaluates the comparative strengths of reduction approaches, and synthesizes state-of-the-art fuzzing techniques including symbolic execution, adaptive fuzzing, and greybox analyses. Through a detailed narrative of redundancy elimination, pattern-based test design, and contract interaction validation, this study highlights the trade-offs between testing efficiency and coverage, providing actionable insights for both practitioners and researchers. Furthermore, the investigation integrates insights from standards such as ISO/IEC 24765 and ISO/IEC 25010 to contextualize quality and reliability within formal frameworks. The paper concludes by outlining future research directions, emphasizing automated quality evaluation, context-aware test reduction, and the intersection of smart contract verification with software engineering best practices.

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References

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Published

2025-08-31

How to Cite

Advancing Test Suite Reduction And Smart Contract Verification: A Comprehensive Analysis Of Techniques, Quality Metrics, And Fuzzing Approaches. (2025). International Journal of Advance Scientific Research, 5(08), 27-33. https://doi.org/10.37547/

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