The rapid growth of blockchain-based applications (BoS) has transformed multiple sectors but also brought significant challenges in software testing, particularly for decentralized applications (DApps) and smart contracts. Current development tools primarily support unit testing and do not address the full range of testing needs for blockchain software. Given the complexity of DApps and the immutability of smart contracts, more advanced methods are required to ensure security, performance, and functional accuracy. This study reviews the current state of blockchain software testing, identifying major gaps and limitations in conventional testing frameworks. To address these challenges, we propose an innovative software testing framework that integrates machine learning to offer real-time, customized testing recommendations for blockchain applications. By utilizing key blockchain features, including distributed ledgers, cryptographic hashing, and decentralized consensus, our model enhances testing accuracy by identifying potential vulnerabilities, performance limitations, and functional discrepancies, reducing the risk of undetected defects in the immutable blockchain environment. Experimental assessments show substantial improvements in testing coverage when compared to established tools such as Truffle and Remix, particularly in the validation of smart contracts and identification of security vulnerabilities. Our framework accelerates the testing process and improves the reliability of blockchain applications by providing developers with comprehensive tools to address the unique challenges of decentralized systems. As blockchain continues to advance in sectors like finance, healthcare, and supply chain management, this study highlights the urgent need for sophisticated testing methods and establishes a foundation for future advancements. By combining machine learning with blockchain testing, we introduce a scalable and adaptable approach that can progress alongside developments in blockchain technology. The conclusion explores broader implications and suggests further improvements, including integration into active deployment pipelines and real-time testing in operational settings. This framework marks a significant advancement in ensuring the dependability, security, and scalability of decentralized blockchain applications, supporting the sustainable growth of these systems in the digital ecosystem.
Cloud-based enterprise resource planning (ERP) systems are replacing traditional on-premise ERP for many companies, in pursuit of cloud benefits such as usage-based licensing, modernized technology and lower costs. While ERP vendors shape their strategy in a software-as-a-service (SaaS) model and promise their customers the aforementioned benefits if they migrate to a subscription-based cloud ERP, we analyse the reality behind these claims and how it affects ERP life cycle costs. This paper focuses on evaluating the total cost of ownership (TCO) between similar IaaS and SaaS systems to provide insight on their key differences. We conduct a multiple case study on four large enterprises that have moved to a SaaS model cloud ERP by analysing archival documents that reflect the actual cost differences between the old and new systems, and provide theoretical implications based on the results. Our findings indicate that the SaaS delivery model can provide cost advantages in specific cases, but IaaS could provide more value for large companies through cost efficiency and more control over the platform vendor. The findings help managers to align their cloud strategy and better understand the possible benefits and pitfalls of subscription-based cloud ERP solutions.
Web usability has become a critical success factor across many organizations in the era of web-based apps. Government websites play a crucial role in distributing information and providing services. However, many websites need more intricate navigation systems and subpar user experiences. BUTTMKP, a governmental agency entrusted with overseeing agricultural quarantine, has recently undertaken a website makeover initiative to address customer feedback and issues. This research's primary objective is to thoroughly evaluate the usability of both the old and revised BUTTMKP web interfaces. The goal is to identify areas that may be enhanced and to recognize the strengths of each design, all from a user-centric standpoint. The research utilizes the System Usability Scale (SUS) and qualitative data-gathering techniques to acquire user input and examine their experiences. The average SUS scores for the old and revised designs stood at 58.5 and 57.8, respectively, and serve as a foundation for our ongoing commitment to improving our model. We recognize that user-friendliness is a crucial aspect of any interface, and these scores encourage us to refine our design further. In the future, we will continue to enhance this model to ensure that it meets our users' evolving needs and expectations.
The application of artificial intelligence (AI) has seen a marked increase in interest, particularly within the Banking, Financial Services, and Insurance (BFSI) sector. Despite this growing interest, many stakeholders remain resistant to adopting AI due to the opaque nature of its outputs, often referred to as a "black box." Explainable Artificial Intelligence (XAI) emerges as a crucial solution, aiming to enhance the transparency and interpretability of AI outputs. Therefore, ensuring the explainability of machine learning outputs is essential in the development of machine learning models. Nevertheless, significant challenges remain in the development of machine learning software applications (MLSA), primarily due to the lack of integration between the machine learning development workflow and the Software Development Life Cycle (SDLC). This disconnect leads to inefficiencies in the MLSA development process and can ultimately result in project failures. This study proposes a software process framework that integrates the machine learning development workflow with the SDLC while ensuring that model outputs are clearly explained in alignment with the needs of MLSA stakeholders. Such integration is necessary to improve MLSA development's overall effectiveness and ensure that the machine learning models' outputs meet the specific requirements and expectations of the stakeholders involved.
