Integrating Human-Computer Interaction and Software Engineering for Enhanced Usability Using Support Vector Machines

Abstract

Software system development is evolving as human-computer interaction (HCI) concepts find application in software engineering. This shift emphasises more the need of knowing user requirements, behaviours, and preferences. This approach is especially important to meet the always rising demand for straightforward-to-run and-understand software systems. On the other hand, traditional methods of software development sometimes ignore the need for usability, which generates products that satisfy less than user expectations. This work suggests a new framework combining HCI methods with software engineering techniques based on the results. Support vector machine (SVM) application helps to improve process usability in development. There are many iterative prototyping, usability testing, and constant user feedback components inside the framework proposed for the software development lifecycle. Strong machine learning (ML) techniques, SVM, are applied for modelling and prediction of user satisfaction based on interaction data acquired during usability testing. Data analysis helps the SVM model find significant factors affecting usability. This enables developers to make sensible decisions exactly matched with consumer expectations. The results of the pertinent tests revealed the degree of improvement in software usability this approach brought about. Apart from a precision of 90.8%, a recall of 91.7%, and an F1-score of 91.2%, the SVM model was evaluated to have reached a classification accuracy of 92.3%. HCI, combined with software engineering, achieved by means of SVM, offers a strong approach for the development of software that not only serves but also strongly connects with users, resulting in a more significant impact on society.