Cross-browser incompatibilities (XBIs) are defined as inconsistencies that can be observed in Web applications when they are rendered in a specific browser compared to others. These inconsistencies are associated with differences in the way each browser implements its capabilities and renders Web applications. The inconsistencies range from minor layout differences to lack of core functionalities of Web applications when rendered in specific browsers. The state of the art proposes different approaches for detecting XBIs and many of them are based on classification models, using features extracted from the document object model (DOM) structure (DOM-based approaches) and screenshots (computer vision approaches) of Web applications. To the best of our knowledge, a comparison between DOM-based and computer vision classification models has not yet been reported in the literature, and a combination between both approaches could possibly lead to increased accuracy of classification models. In this article, we extend the use of these classification models for detecting layout XBIs in responsive design Web applications, rendered on different browser viewport widths and devices (iPhone 12 mini, iPhone 12, iPhone 12 Pro Max, and Pixel XL). We investigate the use of state-of-the-art classification models (Browserbite, CrossCheck, and our previous work) for detecting layout cross-browser failures, which consist of layout XBIs that negatively affect the layout of responsive design Web applications. Furthermore, we propose an enhanced classification model that combines features from different state-of-the-art classification models (DOM based and computer vision) using feature selection. We built two datasets for evaluating the efficacy of classification models in separately detecting external and internal layout failures using data from 72 responsive design Web applications. The proposed classification model reported the highest F1-score for detecting external layout failures (0.65) and internal layout failures (0.35), and these results reported significant differences compared to Browserbite and CrossCheck classification models. Nevertheless, the experiment showed a lower accuracy in the classification of internal layout failures and suggests the use of other image similarity metrics or deep learning models for increasing the efficacy of classification models.

Context: For thousands of years humans have been using natural language to register their knowledge on important information to enable its access to future generations. With internet, a large amount of textual data is produced and shared on a daily basis. So, scientists started to research techniques for efficiently process knowledge stored in textual format. In this context, Natural Language Processing (NLP) became a popular area studying linguistic phenomena and using computational methods to process texts in natural language. In particular, Open Information Extraction (Open IE) was proposed to gather information from plain text. Despite the advances in this area, it is still necessary to map details about how these approaches were proposed to support the community while creating more efficient Open IE systems. Objective: In this paper, we identify, in the literature, the main characteristics of proposed Open IE approaches. Method: First, we extended the search performed in a systematic mapping previously published by using backward snowballing and a manual search. Next, we updated the electronic database search including ACL Anthology. Finally, 159 studies proposing Open IE approaches were considered for data extraction. Results: Data analysis showed a significant increase in the number of studies published about Open IE in the last years. In addition, we provide important details about how these techniques were proposed (e.g., data sets used and output evaluation techniques). Results indicate that researchers started to adopt neural networks to perform Open IE instead of using conventional supervised learning techniques. Conclusion: Recent advances in Artificial Intelligence and neural networks techniques allowed scientists to have a new perspective on how to perform efficient textual data management. Therefore, Open IE approaches gained much attention as they can help in many contexts, especially in knowledge management tasks.

When the same web application is rendered in different browsers, inconsistencies detected in the layout or behavior of pages are known as (XBIs Cross Browser Incompatibilities). Currently, there are different classification models in the literature for the identification and automatic correction of XBIs. These models have evolved with the aim of reducing false positives and negatives. This paper proposes to compare these different models, focusing on those that use the classification of layout XBIs, through machine learning algorithms. There is still no paper in the literature to compare them, identifying their main advantages and disadvantages. This paper consists of an experiment that compares the results of models and presents metrics that allow to affirm how effective they are, aiming also to bring important information as contributions to propose future works regarding the evolution of the explored models. The result of the experiment is the metric of F-Score. For this metric, the higher values imply greater efficiency in detecting incompatibilities between browsers, and the C5.0 10 iterations - X configuration obtained the best result in the experiment.

Web applications can be accessed through a variety of user agent configurations, in which the browser, platform, and device capabilities are not under the control of developers. In order to grant the compatibility of a web application in each environment, developers must manually inspect their web application in a wide variety of devices, platforms, and browsers. Web applications can be rendered inconsistently depending on the browser, the platform, and the device capabilities which are used. Furthermore, the devices’ different viewport widths impact the way web applications are rendered in them, in which elements can be resized and change their absolute positions in the display. These adaptation strategies must also be considered in automatic incompatibility detection approaches in the state of the art. Hence, we propose a classification approach for detecting Layout Cross-platform and Cross-browser incompatibilities, which considers the adaptation strategies used in responsive web applications. Our approach is an extension of previous Cross-browser incompatibility detection approaches and has the goal of reducing the cost associated with manual inspections in different devices, platforms, and browsers, by automatically detecting Layout incompatibilities in this scenario. The proposed approach classifies each DOM element which composes a web application as an incompatibility or not, based on its attributes, position, alignment, screenshot, and the viewport width of the browser. We report the results of an experiment conducted with 42 Responsive Web Applications, rendered in three devices (Apple iPhone SE, Apple iPhone 8 Plus, and Motorola Moto G4) and browsers (Google Chrome and Apple Safari). The results (with F-measure of 0.70) showed evidence which quantify the effectiveness of our classification approach, and it could be further enhanced for detecting Cross-platform and Cross-browser incompatibilities. Furthermore, in the experiment, our approach also performed better when compared to a former state-of-the-art classification technique for Cross-browser incompatibilities detection.

The Web 2.0 brought new requirements to the architecture of web systems. Web applications’ interfaces are becoming more and more interactive. However, these changes are severely impacting how disabled users interact through assistive technologies with the web. In order to deploy an accessible web application, developers can use WAI-ARIA to design an accessible web application, which manually implements focus and keyboard navigation mechanisms. This article presents a quantitative metric, named Fona, which measures how the Focus Navigation WAI-ARIA requirement has been implemented on the web. Fona counts JavaScript mouse event listeners, HTML elements with role attributes, and TabIndex attributes in the DOM structure of webpages. Fona’s evaluation approach provides a narrow analysis of one single accessibility requirement. But it enables monitoring this accessibility requirement in a large number of webpages. This monitoring activity might be used to give insights about how Focus Navigation and ARIA requirements have been considered by web development teams. Fona is validated comparing the results of a set of WAI-ARIA conformant implementations and a set of webpages formed by Alexa’s 349 top most popular websites. The analysis of Fona’s value for Alexa’s websites highlights that many websites still lack the implementation of Focus Navigation through their JavaScript interactive content.