• WEs with high similarity guided developers to relevant directories and files. • Eye-tracking revealed the benefits and challenges of using WEs in OSS projects. • Data beyond the title and description is needed to obtain more relevant WEs. • WEs served as a starting point for resolving issues on GitHub. • WEs complement LLMs by offering community-validated solutions. The growing popularity of Open-Source Software projects has raised questions about the challenges novice and inexperienced developers face, especially on code contribution platforms like GitHub. This study investigates the effects of using Worked Examples (WEs) to support these developers in solving coding tasks, using eye-tracking and cognitive effort analysis. The research involved 20 undergraduate students analyzing issues from the JabRef repository, with recommendations of high and low-similarity examples provided by a bot. The findings suggest that highly similar WEs effectively guided participants by helping identify relevant directories, files, and code snippets, serving as starting points for task resolution. However, challenges emerged, such as difficulties locating useful information and risks of false proximity between seemingly similar issues. These results highlight the need for improved recommendation strategies beyond textual similarity, incorporating structural elements such as file and method names, while reducing cognitive load through better presentation of relevant information. This work lays the groundwork for exploring WEs in Open-Source Software projects and opens avenues for further research, including validating findings in other repositories and understanding behavioral patterns in using WEs.

User eXperience (UX) evaluations play an essential role in the software development process. As the results from such evaluations can drive future releases, it is necessary to identify which factors can substantially change users’ judgments about their experience to have more precise results and understand UX better. This article investigates how interaction sequencing, previous experience, and the number of problems could affect overall satisfaction and the two main UX dimensions: pragmatic and hedonic. We employed three different evaluation methods to evaluate a chatbot-based mobile shopping application. The results revealed that participants with previous experience with similar apps tended to give lower ratings. We also found that as inspectors identify more problems, they tend to rate the pragmatic dimension lower. Finally, we did not identify a significant influence of interaction sequencing on UX evaluation. We discuss the reasons for these results, the implications for practitioners and researchers, and research opportunities.

User experience (UX) is a quality aspect of an application that considers the emotions evoked by the system.There are several types of UX evaluation methods, such as scales, interviews, user monitoring, among others methods.However, there is still not enough information regarding if and in which contexts these methods are more suitable.This paper focuses on finding more information about the feasibility of applying scaletype methods for the UX evaluation in interactive systems.Thus, we carried out a UX evaluation on the Edmodo educational mobile application employing two scale-type methods: AttrakDiff and Hedonic Utility Scale.These methods were chosen after a selection process applying inclusion and exclusion criteria.The results indicate that it is possible to evaluate the quality of an application at a low cost.However, it is necessary to adapt these methods to provide a more complete report of the UX, allowing users to subjectively report their experiences and, consequently, identify the issues that affected the UX.