This study explores the implementation of the analytic hierarchy process in usability evaluations, specifically focusing on user interface assessment during software development phases. Addressing the challenge of diverse and unstandardized evaluation methodologies, our research develops and applies a tailored algorithm that simplifies heuristic prioritization. This novel method combines the analytic hierarchy process framework with a bespoke algorithm that leverages transitive properties for efficient pairwise comparisons, significantly reducing the evaluative workload. The algorithm is designed to facilitate the estimation of heuristic relevance regardless of the number of items per heuristic or the item scale, thereby streamlining the evaluation process. Rigorous simulation testing of this tailored algorithm is complemented by its empirical application, where seven usability experts evaluate a web interface. This practical implementation demonstrates our method’s ability to decrease the necessary comparisons and simplify the complexity and workload associated with the traditional prioritization process. Additionally, it improves the accuracy and relevance of the user interface usability heuristic testing results. By prioritizing heuristics based on their importance as determined by the Usability Testing Leader—rather than merely depending on the number of items, scale, or heuristics—our approach ensures that evaluations focus on the most critical usability aspects from the start. The findings from this study highlight the importance of expert-driven evaluations for gaining a thorough understanding of heuristic UI assessment, offering a wider perspective than user-perception-based methods like the questionnaire approach. Our research contributes to advancing UI evaluation methodologies, offering an organized and effective framework for future usability testing endeavors.

This study delves into the realm of heuristic evaluation within usability testing, focusing on refining and applying a tailored algorithm that simplifies the implementation of the Analytic Hierarchy Process for heuristic prioritization. Addressing the prevalent challenge of disparate evaluation methodologies and the absence of a standardized process in usability testing, our research introduces a novel method that leverages the Analytic Hierarchy Process alongside a bespoke algorithm. This algorithm utilizes transitive properties for pairwise comparisons, significantly reducing the evaluative workload. This innovative approach facilitates the estimation of heuristic relevance irrespective of the number of items per heuristic or the item scale and streamlines the overall evaluation process. In addition to rigorous simulation testing of the tailored algorithm, we applied our method in a practical scenario, engaging seven usability experts in evaluating a web interface. This empirical application underscored our method’s capacity to diminish the requisite comparison count and spotlight areas of improvement for critically weighted yet underperforming heuristics. The outcomes of this study underscore the efficacy of our approach in enhancing the efficiency and organization of heuristic evaluations, paving the way for more structured and effective usability testing methodologies in future research endeavors.

The number of traffic accidents involving motorcycles has been increasing in Brazil recently. Many accidents are caused by drivers who do not see motorcycles approaching in the vehicle blind spots. Advanced Driver Assistance Systems (ADAS) installed in vehicles can be used to mitigate this problem. However, the development of ADASs often focuses on security issues and does not consider the user experience with the ADASs interface. In this paper, we present the design of an alerting system that warns drivers about collision risks when motorcycles are identified in vehicle blind spots. Our proposal alerts drivers by using visual and haptic interaction modes. In line with a user-centered design methodology, our initial action involved identifying the traits of the target users in order to create personas that embody their characteristics. The vehicle blind spot alert system was conceived in a co-design session using the personas elaborated previously. In the session, 9 end-users produced 3 low-fidelity (lo-fi) prototypes which were analyzed and compiled generating a single lo-fi prototype. After promoting a viability discussion, a high-fidelity (hi-fi) prototype containing haptic and visual alerting features was implemented and installed in a car for testing. The alert system was evaluated by 20 end-users concerning their experience with the different warning modes. The results showed that for both the visual and haptic modes, users could recognize and understand the alerts without employing a great effort in the information interpretation. This result reinforces the idea that ADASs should provide simple interpretative interfaces because drivers' interaction with these systems should be a secondary activity since their concentration must be on driving.