Genomecmp: computer software to detect genomic rearrangements using markers

Software testing has become an important phase in software applications’ lifecycle. Graphical User Interface (GUI) components can be found in a large number of desktops and web applications and also in a wide variety of mobile devices. In the last years GUIs have become more and more complex and interactive, their testing process requiring an interaction with the GUI components, mainly by generating mouse, keyboard and touch events. Given their increased importance, GUIs verification for correctness contributes to the establishment of the correct functionality of the corresponding software application. The current research on GUI testing methodologies primarily focuses on automated testing. This paper presents EvoGUITest, a novel automated GUI testing framework based on evolutionary algorithms which tests the GUI independently from the application code itself. The framework is designed for testing GUIs of web applications. Results have been compared, based on specific metrics, with others existing frameworks.

Web and Event-driven applications (EDS) is a class of applications that is quickly becoming ubiquitous. All EDS take sequences of events (e.g., messages, mouse-clicks) as input, change their state, and produce an output (e.g., events, system calls, text messages), where as in web, user session data gathered as users operate web applications can be considered as input, change their state, and produce an output. Examples include web applications, graphical user interfaces (GUIs), network protocols, device drivers, and embedded applications. Testing for functional correctness of EDS such as stand-alone GUI and web-based applications is critical to many organizations. These applications share several important characteristics. Both are particularly challenging to test because users can invoke many different sequences of events that affect application behavior. Hence here a novel model is provided to rank the test cases based on their prioritization.

Automated testing in web applications is becoming increasingly popular due to its cost-effectiveness, efficiency, and repeatability. Ensuring the quality of such software is vital, especially in scenarios where business requirements are uncertain and volatile. In this paper, we propose Cytestion, a tool that automatically explores a web application’s Graphical User Interface (GUI) to detect faults that cause visible failures, such as crashes, errors, and unexpected behaviors. For that, it applies a scriptless and progressive approach. To work as an oracle, Cytestion evaluates the output of each test case execution by considering the HTTP status of server requests, default and customized failure GUI messages, and console failure messages. We evaluated the effectiveness and cost of our approach in two empirical studies. The first study works with injected faults in open-source projects, while the second is a case study on eight industrial projects. Cytestion detected several faults in both studies and outperformed a state-of-the-art GUI testing tool.

Context: The growing complexity of modern web and mobile applications, shaped by rapid UI/UX advancements and agile software development practices, demands efficient and automated recognition of graphical user interface (GUI) elements, as manual testing remains costly and time-consuming. Objective: This study aims to evaluate the performance of the YOLOv8 deep learning architecture for accurate recognition of diverse GUI components, such as buttons, fields, headings, links, and images, from real-world application screenshots. Method: A small YOLOv8 model was trained on the Roboflow GUI element detection dataset containing over 1,000 annotated website screenshots, using 35 training epochs, a batch size of 8, and an image resolution of 640×640. Results: The model achieved measurable improvements, with precision rising from 0.368 to 0.454, recall increasing from 0.296 to 0.425, and mAP50–95 improving from 0.101 to 0.232. Strongest detection was observed for buttons and input fields, while weaker performance was noted for iframes and labels due to their inherent ambiguity. Conclusions: The results demonstrate that YOLOv8 has significant potential in automating GUI recognition, reducing reliance on manual testing in agile workflows, and improving interface validation. Further optimization with larger datasets and advanced augmentation methods is recommended to enhance robustness and generalization

Mobile applications use Accessibility Services to assist users with disabilities (UWDs) in using Graphical User Interface (GUI)-based apps. However, such mobile assistive technologies are not fully secure as these can obtain data from the GUI objects, enhance this data for UWDs and transmit it to a server, thereby potentially exposing sensitive data to the external world. SEAPHISH (SEcuring Accessibility using PHISHing) is a platform aimed towards protecting against such an attack by providing defense by deception. This platform generates a phishing app i.e. an app similar to the original app installed on the user’s smartphone by extracting GUI elements and properties from the original app. A simulation for SEAPHISH can help determine the situations when an attack against a particular app can be performed with a high degree of probability. But performing effective simulations requires a fundamental understanding of the properties of GUI layouts of apps at large. This thesis aims at providing a framework that analyzes GUI layouts and their transitions using a large base of approximately three million Android apps. Various state-of-the-art tools that use different strategies in traversing layouts are explored. We created a framework where the tool Backstage performs static analysis and another tool AndroidRipper performs dynamic analysis of the layouts to help build a GUI model of the app. Using these models, we investigate the layouts of Android apps by collecting statistics on various GUI elements and screens. This investigation enhances SEAPHISH with statistically significant real-world constraints thereby providing defense against malwares and reducing the security vulnerabilities faced by UWDs.

The article presents methods and algorithms for improving the visualization of a graphical user interface using augmented reality in the field of education. A general diagram of the application structure is presented, including modules: graphical user interface, web application, visualization block. The diagram shows the «camera» object; it allows you to obtain an image of the real environment, which is sent to the visualization unit and the image processing unit. The internal techniques used by the mobile application for visualizing augmented reality are considered: visualization of an object in accordance with the angle, combination of the object with the image of the real environment. Combining an object with a real-world environment allows students to observe 3D objects or models and conduct interactive experiments, enhancing learning. The application being developed is based on the use of diffuse lighting techniques, specular reflection, refraction and other aspects of physical lighting. This allows you to create visually appealing scenes that take into account the complex interactions of light with the surface of objects. The functions of the graphical user interface block are considered, namely logging into the system, loading new objects, manipulating objects in space. User groups and the functions available to them are highlighted. Application user groups: teacher (access rights: viewing information, managing courses, feedback, viewing work); student (access rights: viewing information, submitting work). The modules and components of the web application are identified and described: application server, action form, data model components, database. Conclusions are drawn about the prospects of integrating augmented reality technologies into the graphical user interface in the field of education.

Visual and aural are two most important channels of information processing. While most of the interaction with computers have been designed around the visual channel, there are circumstances where voice based man-machine interaction becomes preferable, and in some cases, necessary, given that voice based interaction comes naturally to humans and can be used by illiterate people easily.

The contemporary theory of metaphor defines the metaphor as a major and indispensable part of our ordinary, conventional way of conceptualizing the world and it asserts that the metaphors are used to define our relationship with the physical environment and to create a context of communication. The metaphors are the core idioms of the contemporary graphical user interface (GUI) and have an important role in helping users interact with computer systems. The desktop metaphor began with the first GUI Xerox Star and was popularized by the Lisa of Apple Macintosh. Realizing the physical-office metaphor and reflecting the familiar world of the potential users by using icons of file, folder and trashcan, the metaphor-based GUIs made it relatively easy for all to use the computer productively. Nowadays almost all of the software industry is based on the desktop metaphor paradigm. However, besides the researchers that favor the use of metaphors in the development of the system image by accentuating their assistance to learning, there are also other researchers, who reject the metaphor-based design approach in GUIs by referring to their limitations. Considering these discussions and various approaches, this study aims to make an outline of the historical development of metaphor-based GUIs; to determine the current state of metaphor within GUI design by brief examples and finally to identify future prospects and research themes. The study claims that metaphor still remains to be an inevitable part of the GUI to communicate it with the users and despite its potential limitations -rather than rejecting the metaphor totally- it seems better-to try compatible and supplementary solutions like visual formalism.

PETOOL v2.0: Parabolic Equation Toolbox with evaporation duct models and real environment data

BackgroundFine-mapping projects require a high density of SNP markers and positional candidate gene sequences. In species with incomplete genomic sequence, the DNA sequences needed to generate markers for fine-mapping within a linkage analysis confidence interval may be available but may not have been assembled. To manually piece these sequences together is laborious and costly. Moreover, annotation and assembly of short, incomplete DNA sequences is time consuming and not always straightforward.FindingsWe have created a tool called BEAP that combines BLAST and CAP3 to retrieve sequences and construct contigs for localized genomic regions in species with unfinished sequence drafts. The rational is that a completed genome can be used as a template to query target genomic sequence for closing the gaps or extending contig sequence length in species whose genome is incomplete on the basis that good homology exists. Each user must define what template sequence is appropriate based on comparative mapping data such as radiation hybrid (RH) maps or other evidence linking the gene sequence of the template species to the target species.ConclusionThe BEAP software creates contigs suitable for discovery of orthologous genes for positional cloning. The resulting sequence alignments can be viewed graphically with a Java graphical user interface (GUI), allowing users to evaluate contig sequence quality and predict SNPs. We demonstrate the successful use of BEAP to generate genomic template sequence for positional cloning of the Angus dwarfism mutation. The software is available for free online for use on UNIX systems at .

Mobility for graphical user interfaces (GUIs) is a challenging problem, as different GUIs need to be constructed for different device capabilities and changing context, preferences and users’ locations. GUI developers frequently create multiple user interface versions for different devices. The solution lies in using a single, abstract, user interface description that is used later to automatically generate user interfaces for different devices. Various techniques are proposed to adapt GUIs from an abstract specification to a concrete interface. Design-time techniques have the possibility of creating better performing GUIs but, in contrast to run-time techniques, lack flexibility and mobility. Run-time techniques’ mobility and autonomy can be significantly improved by using mobile agent technology and an indirect GUI generation paradigm. Using indirect generation enables analysis of computer-human interaction and application of artificial intelligence techniques to be made at run-time, increasing GUIs’ performance and usability.

Graphical User Interfaces (GUIs) are amongst the most common user interfaces, enabling interactions with applications through mouse movements and key presses. Tools for automated testing of programs through their GUI exist, however they usually rely on operating system or framework specific knowledge to interact with an application. Due to frequent operating system updates, which can remove required information, and a large variety of different GUI frameworks using unique underlying data structures, such tools rapidly become obsolete, Consequently, for an automated GUI test generation tool, supporting many frameworks and operating systems is impractical. We propose a technique for improving GUI testing by automatically identifying GUI widgets in screen shots using machine learning techniques. As training data, we generate randomized GUIs to automatically extract widget information. The resulting model provides guidance to GUI testing tools in environments not currently supported by deriving GUI widget information from screen shots only. In our experiments, we found that identifying GUI widgets in screen shots and using this information to guide random testing achieved a significantly higher branch coverage in 18 of 20 applications, with an average increase of 42.5% when compared to conventional random testing.

Dynamic Software Product Line (DSPL) Engineering has gained interest through its promise of being able to unify software adaptation whereby software adaptation can be realised at compile time and runtime. While previous work has enabled program logic adaptation by the use of language extensions and platform support, little attention has been placed on Graphical User Interface (GUI) variability. Different design patterns including the Model View Controller are commonly used in GUI implementation, with GUI documents being used for declaring the GUI. To handle dynamic GUI variability currently, the developer needs to implement GUI refinements using multiple techniques. This paper proposes a solution for dealing with GUI document variability, statically and dynamically, in a unified way. In our approach, we currently use a compile time method for producing GUI variants, and code transformations to handle these variants within the application at runtime. To avoid GUI duplicates, only GUI variants that are unique, and related to a valid product configuration, are produced. To validate our approach, we implemented tool support to enable this for Android based applications.

An important component of Early Warning Systems (EWS) for man-made and natural hazards is the command and control unit’s Graphical User Interface (GUI). All relevant information of an EWS is concentrated in this GUI and offered to human operators. However, when designing the GUI, not only the user experience and the GUI’s screens are relevant, but also the frameworks and technologies that the GUI is built on and the implementation of the GUI itself are of great importance. Implementations differ based on their applications in different domains but the design and approaches to implement the GUIs of different EWS often show analogies. The design and development of such GUIs are performed repeatedly on some parts of the system for each EWS. Thus, the generic GUI framework of a geospatial EWS for tsunamis is introduced to enable possible synergistic effects on the development of other new related technology. The results presented here could be adopted and reused in other EWS for man-made and natural hazards.

An Automated Tool To Detect Sentiment-Based Spam for Text Using Machine Learning