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Author: Yazici, AliScopus Q: N/A

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    Smart Contract Upgradability: a Structured and Natural Approach
    (Institute of Electrical and Electronics Engineers Inc., 2024) Culha, Davut; Yazici, Ali
    Software maintenance is crucial as technology rapidly evolves, requiring software to meet new demands and correct errors. Smart contracts, immutable programs on blockchains like Ethereum, face challenges despite their immutability, often needing updates for errors or new features. Smart contracts are upgraded using different patterns, which are not natural because most of them implement upgrades using low-level operations that deviate from their intended use. In other words, these patterns are not natural because upgrades are done by implementing workarounds. Moreover, smart contracts are also susceptible to security vulnerabilities because they may hold large amounts of money. In this paper, upgradability of smart contracts is considered a necessity. For this purpose, a more structured method is proposed by adding high-level features and combining inheritance properties of object-oriented languages. A key component of this method is the gotoContract variable, which allows for the redirection of function calls to upgraded contracts. The proposed method provides a complete upgrade of data and functions in smart contracts. It aims to minimize the effects of upgrades on end users of the smart contracts. Additionally, this natural way of upgrading will help mitigate security risks in the smart contracts by providing a high-level approach to upgrade.
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    Hybrid AI-Driven Decision Model for Test Automation in Agile Software Development
    (Institute of Electrical and Electronics Engineers Inc., 2025) Bon, Mohammad; Yazici, Ali
    Test automation plays an essential role in Agile Software Development (ASD), but its implementation remains complex. This study conducts a Systematic Literature Review (SLR) to identify key points of test automation and recent developments in Artificial Intelligence (AI). Based on 21 factors proposed by Butt et al., we construct a three-phase decision-support model addressing software, tools, tests, human, and economic dimensions. To improve this model, modern AI techniques - including natural language processing (NLP), machine learning (ML), Mabl (a self-healing, AI-based test automation tool) and Parasoft Selenic - are used. These technologies automate test case generation, prioritization, and maintenance, aligning with Agile's fast-paced demands. Our proposed hybrid model applies NLP to identify effecting factors, ML for impact scoring, and reinforcement learning (RL) for guiding automation strategies. The goal is to decrease manual processes, improve decision accuracy, and to adapt to evolving requirements. However, challenges such as data quality and the need for AI expertise remain. Future work should focus on practical validation and explore applications in non-functional testing. This study offers a practical, AI-enhanced framework to support Agile teams in streamlining test automation. © 2025 IEEE.