Accurate software project cost estimation is essential for efficient resource allocation, risk management, and scheduling. Accuracy and generality are frequently lacking in traditional estimate models. To improve prediction accuracy, this study suggests a hybrid framework (Fuzzy + GA) that combines fuzzy logic and genetic algorithms. Evaluations were performed using reference datasets from Desharnais, Kitchenham, and Maxwell with RMSE values of 0.4531, 0.0312, and 0.0416 and R squared scores of 0.7513, 0.9512, and 0.9142, respectively. The model outperformed current techniques and demonstrated notable gains.

This Research Paper seeks to examine the possibilities of blockchain technology. For use in the field of restaurant food tracking and safety. Public health risks and economic costs are at stake when foodborne illness outbreaks occur, making food safety a top priority in the food industry. It can be difficult to quickly identify and address possible concerns about using traditional food traceability systems due to inefficiencies, data discrepancies, and a lack of transparency. In this study, we introduce a novel blockchain-based system developed especially for the purpose of tracking restaurant food. The blockchain decentralised consensus, immutability, and smart contracts are put to use in this system to provide trustworthy and transparent traceable infrastructure. Real-time monitoring and data collection along the food supply chain become possible when the blockchain architecture is combined with the Internet of Things (IoT) devices and RFID technology. We show that our proposed blockchain-based traceability solution is practical and efficient through a thorough assessment and validation procedure. The outcomes show that the system not only improves data quality and reliability but also drastically decreases the time and resources needed for food traceability. In addition, patrons are more likely to return to eateries that place a premium on food safety when they are given more information about the establishment's practises. Additionally, we discuss scalability, data privacy, and interoperability concerns that may arise in future implementations and provide some first ideas for overcoming these issues.

Abstract Whatever be the nature of underlying software, the impact of the software development process that was used to create it would remain vital. The objective of this paper is to provide a process selection framework for the software development firm's engineers and managers, looking to identify the proper way to build software to run on a mobile, web, or desktop. The motivation behind this work comes from the fact that the applications that fall in one of the above three categories can be significantly different in terms of scale, UI, and memory requirements, time to market, and other characteristics. Software development that needs to be completed in a challenging timeframe has to resort to principles and values as declared in Agile Manifesto. The availability of various agile methodologies has made the project managers often stuck when selecting the most suitable one. The proposed agile process identification system addresses this dilemma of engineering fraternity using a fuzzy variant of a popular multiple‐criteria decision‐making (MCDM) technique called the analytic hierarchy process (AHP). The proposed system is validated through a primary dataset generated as a result of the development of 20 software projects. The results are encouraging enough with a probability of true identification close to 88%.

Software cost estimation is one of the most crucial tasks in a software development life cycle. Some well-proven methods and techniques have been developed for effort estimation in case of classical software. Mobile applications (apps) are different from conventional software by their nature, size and operational environment; therefore, the established estimation models for traditional desktop or web applications may not be suitable for mobile app development. The objective of this paper is to propose a framework for mobile app project estimation. The research methodology adopted in this work is based on selecting different features of mobile apps from the SAMOA dataset. These features are later used as input vectors to the selected machine learning (ML) techniques. The results of this research experiment are measured in mean absolute residual (MAR). The experimental outcomes are then followed by the proposition of a framework to recommend an ML algorithm as the best match for superior effort estimation of a project in question. This framework uses the Mamdani-type fuzzy inference method to address the ambiguities in the decision-making process. The outcome of this work will particularly help mobile app estimators, development professionals, and industry at large to determine the required efforts in the projects accurately.

Software industry is turning toward endorsing application (app, in short) development due to the ubiquitous use and interest in this computing pattern. Increasing trend and popularity of mobile apps reveal several issues for the developers to address. Absence of a scientific developmental approach adds further issues to the apps development. There are millions of daily downloads, use and views on mobile apps, which give rise to an interesting phenomenon of apps acceptance by the user community. It is significant to note that users tend to reject or dislike apps that present challenges, owing to the issues in the apps, to them. Therefore, it is imperative to know the different issues that affect mobile ratings. In this paper, we have identified 14 issues by reviewing the relevant literature and collected the data from numerous mobile app stores to find the influence of the identified issues on mobile app ratings. Further, an interpretive structure modeling (ISM) approach is used to categorize the identified issues into four groups — dependent, driving, linkage and autonomous — for better understanding and further analysis. There are two objectives of this research paper: (1) to identify issues in apps which affect ratings and (2) to find out mutual relationship between dominating issues in mobile apps.