Abstract We investigate how tutorials inspired by video game design can improve the adoption and effective use of static analysis tools in software development. Static analysis offers significant benefits for code quality and maintainability; however, its adoption is hindered by steep learning curves and time-intensive on boarding processes. Drawing parallels to gamers who prefer quick and engaging tutorials, the authors propose embedding interactive tutorials in... We investigate how tutorials inspired by video game design can improve the adoption and effective use of static analysis tools in software development. Static analysis offers significant benefits for code quality and maintainability; however, its adoption is hindered by steep learning curves and time-intensive on boarding processes. Drawing parallels to gamers who prefer quick and engaging tutorials, the authors propose embedding interactive tutorials in static analysis tools to lower entry barriers. Their research strategy includes three phases: (1) a pilot study using the Wizard-of-Oz method to simulate a tutorial via human guidance and identify user needs, (2) the development of a prototype tutorial within the open-source tool FindBugs using a gamified approach (e.g., storytelling and rewards), and (3) an evaluation comparing user performance and engagement with and without the enhanced tool. The goal is to reduce training time, improve comprehension of warnings, and raise awareness of software quality. The study anticipates that gamified tutorials can not only accelerate tool learning but also educate users on deeper software engineering principles. Future work includes long-term classroom testing and addressing usability issues such as false positives and poor interface design. Read More Read Less

Abstract Rapid shifts in customer preferences were the product of current developments. Larger outlets and small outlets usually remain accessible, but larger malls, warehouses, and shopping centres are also shut down. Many consumers are panic-buying products out of concern for the complete run-down of commodities. There is a drastic effect on customers’ outlook, attitudes and actions. Change in consumer preferences is a consequence of the... Rapid shifts in customer preferences were the product of current developments. Larger outlets and small outlets usually remain accessible, but larger malls, warehouses, and shopping centres are also shut down. Many consumers are panic-buying products out of concern for the complete run-down of commodities. There is a drastic effect on customers’ outlook, attitudes and actions. Change in consumer preferences is a consequence of the rapid pace of significant developments. The big pharmacies and supermarkets are now abandoned, but that is not the end of things. Malls, factories, and some convenience shops are all closed. Many buyers are purchasing goods out of fear of a total collapse of costs, commodities, and materials. This research would concentrate on the consumption behaviour after the COVID-19 pandemic comes to an end and stabilises the market condition. By ensuring data from COVID-19-affected nations, it would be possible to forecast potential business and economic changes. Some countries would not be able to follow the same path, but the examination in such areas would be invaluable. Read More Read Less

Abstract The paper introduces an incremental approach to training Hidden Markov Models (HMMs), particularly aimed at modeling discrete-time workloads such as I/O traces. Traditional HMM training, notably using the Baum-Welch algorithm, requires the full dataset in advance, which limits its applicability in real-time applications. To address this, the authors develop IncHMM, a model that updates its parameters incrementally as new data arrives, making it suitable... The paper introduces an incremental approach to training Hidden Markov Models (HMMs), particularly aimed at modeling discrete-time workloads such as I/O traces. Traditional HMM training, notably using the Baum-Welch algorithm, requires the full dataset in advance, which limits its applicability in real-time applications. To address this, the authors develop IncHMM, a model that updates its parameters incrementally as new data arrives, making it suitable for dynamic workloads. Central to their method is creating two approximations of the backwards ( ) variables in the Forward- Backwards algorithm, which enables the model to process new observations without reprocessing the entire dataset. The first approximation assumes convergence properties in state probabilities, while the second employs matrix inversion techniques with fallback strategies when certain conditions are not met. Both are tested on real-world I/O traces that have been pre-processed using the K-means clustering algorithm. Simulation results show that the incrementally trained HMMs closely match the statistical properties (mean and standard deviation) of the raw data, especially for read operations. Write discrepancies are noted and attributed to lower variance or model limitations. The authors suggest future improvements, including refining the clustering algorithm and testing it on other time series, such as hospital arrival data, to enhance versatility and accuracy. Read More Read Less