Session-Based Recommender Systems for Action Selection in GUI Test Generation

Test generation at the graphical user interface (GUI) level has proven to be an effective method to reveal faults. When doing so, a test generator has to repeatably decide what action to execute given the current state of the system under test (SUT). This problem of action selection usually involves random choice, which is often referred to as monkey testing. Some approaches leverage other techniques to improve the overall effectiveness, but only a few try to create human-like actions---or even entire action sequences. We have built a novel session-based recommender system that can guide test generation. This allows us to mimic past user behavior, reaching states that require complex interactions. We present preliminary results from an empirical study, where we use GitHub as the SUT. These results show that recommender systems appear to be well-suited for action selection, and that the approach can significantly contribute to the improvement of GUI-based test generation.Comment: 5 pages, 3 figures, to be published in ICSTW 202

Implementación de un sistema basado en aprendizaje reforzado para la realización de pruebas autónomas de aplicaciones web desde su interfaz gráfica

"Las aplicaciones web juegan un papel muy importante en el mundo actual, a raíz de que las empresas y organizaciones comenzaron a adoptar la World Wide Web para ofrecer sus servicios y con la adopción masiva de la internet, las aplicaciones web han cambiado por completo nuestras vidas, la economía y la sociedad en general. Por lo cual es de gran relevancia garantizar el correcto funcionamiento de estos sistemas, si bien es una práctica estándar la realización de pruebas automatizadas en la industria del software, su implementación y verificación continúan dependiendo de la intervención humana y por lo tanto susceptibles a posibles errores. Este trabajo propone desarrollar un sistema que permita la realización de pruebas a una aplicación web desde su interfaz web de forma completamente autónoma, auxiliándose en herramientas como lo es el aprendizaje por refuerzo y árbol de búsqueda, si bien el alcance de este trabajo se encuentra limitado por diferentes factores como el tipo de aplicaciones compatibles y los tipos de errores que son posibles detectar, se introduce una herramienta que puede auxiliar y complementar los proceso de pruebas actuales, aumentando el grado de cobertura de pruebas en una aplicación web y por lo tanto incrementando su calidad"

Users’ Sentiment Analysis toward National Digital Library of India: a Quantitative Approach for Understanding User perception

Sentiment analysis is also known as opinion mining. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in textual data. It is extremely used by business, educational organizations, and social media monitoring to gain the general outlook of the wide public regarding their product and policy. The current study looks for gaining insights into user reviews on the National Digital Library of India (NDLI) mobile app (android and iOS). For this purpose, sentiment analysis will be used. It yields an average of 3.64/5 ratings based on 11,861 reviews. The dataset includes a total of 4560 user reviews in which iOS and the android app have received 33 and 4527 reviews respectively as on 7th Sept 2021. AppBot and AppFollow analytics software is used to extract and collect user review information as raw data. The study shows the reviews of the NDLI mobile app as 2130 positive and 1808 negative sentiments for android & 6 positive and 22 negative sentiments for iOS. The overall sentiment score is found to be 66%. The results of the sentiment analysis show that Android users are more satisfied as compared to iOS users. The most frequent complaints made by the users are functional errors, feature requests and app crashes. Some of the major issues that users have complained about are books that need to be downloaded before reading and some pdfs are blank once opened. The value of this research is getting an insight into the behaviour of users towards using apps on different platforms (Android vs iOS) and provides valuable results for the app developers in monitoring usage and enhancing features for the satisfaction of users. The findings reveal that stakeholders/developers need to pay more attention to make the app more user-friendly