GUI Model Research Articles

Development of a machine learning model to predict the friction stir welding tool condition

Tool damage results in surface polishing and dimensional accuracy losses for machined parts, as well as potential damage to a workpiece or machine. In the industrial business, tool condition monitoring (TCM) is significant. As a result, it is essential for production efficiency and profitability. In order to prevent damages from occurring, also known as predictive maintenance, we may use the TCMS to identify tool wear and damage that eventually impacts the workpiece. Machine learning models play a significant part in TCMS by way of a GUI that identifies the tool's failure state. This method can be applied in any kind of system which is used in the industry to predict the damage. This TCMS is applied in a friction stir welding machine (FSW) to predict its tool wear. Friction stir welding, an eco-friendly solid state welding method, is used to join two metals that are difficult to fuse together using conventional fusion welding. FSW is largely recognized as the most important innovation in metal joining technology in the previous decade. This FSW has been widely employed in numerous sectors such as shipbuilding, computer covers, vehicles, and so on. Developing a GUI for the prediction of tool condition in FSW with AZ31B(Mg) as a workpiece is a novel approach. In this condition monitoring technique, the real time vibrational data is collected with the help of an accelerometer. These data’s are being processed and features are extracted and a proper classifier is used to predict the faults. This paper aims to develop a part of TCMS through vibrational data. GUI and ML models were developed to predict the condition of FSW tools. The ML model includes Decision Tree, Random Forest, Light Gradient Boosted Machine Classifiers. Among all these Light Gradient Boosted Machine Classifiers perform better. Using this Classifier a GUI is built to predict the condition.

Semantic Similarity-Based Mobile Application Isomorphic Graphical User Interface Identification

Applying robots to mobile application testing is an emerging approach to automated black-box testing. The key to supporting automated robot testing is the efficient modeling of GUI elements. Since the application under testing often contains a large number of similar GUIs, the GUI model obtained often contains many redundant nodes. This causes the state space explosion of GUI models which has a serious effect on the efficiency of GUI testing. Hence, how to accurately identify isomorphic GUIs and construct quasi-concise GUI models are key challenges faced today. We thus propose a semantic similarity-based approach to identifying isomorphic GUIs for mobile applications. Using this approach, the information of GUI elements is first identified by deep learning network models, then, the GUI structure model feature vector and the semantic model feature vector are extracted and finally merged to generate a GUI embedding vector with semantic information. Finally, the isomorphic GUIs are identified by cosine similarity. Then, three experiments are conducted to verify the generalizability and effectiveness of the method. The experiments demonstrate that the proposed method can accurately identify isomorphic GUIs and shows high compatibility in terms of cross-platform and cross-device applications.

Material optimization for in cab thermal energy management

Human Body operates to the maximum efficiency at ambient temperature of 20–30 °C (68–86 °F).Hence it is essential to maintain in cab temperature near about this optimum operating range. In cab temperature is influenced by heat generated from heat sources like Engine, exhaust system, In-cab ECU’s, Sun load from Cab Openings & other environment factors. In this paper, an intelligible model for the vehicle in cab temperature predictions is depicted. The proposed GUI model can be employed for calculating in cab temperature at identified touch points & non touch points in vehicle. When coupled with a typical organizational material database this GUI model can also be deployed to predict optimum material options to achieve desired results.GUI model is built using MATLAB. The mathematical equation used for the model is based on basic principles of heat transfer. This GUI Model can only be used in steady state condition.

Historical Civil Registration Record Transcription Using an eXtreme Model Driven Approach

Modelling is considered as a universal approach to define and simplify real-world applications through appropriate abstraction. Model-driven system engineering identifies and integrates appropriate concepts, techniques, and tools which provide important artefacts for interdisciplinary activities. In this paper, we show how we used a model-driven approach to design and improve a Digital Humanities dynamic web application within an interdisciplinary project that enables history students and volunteers of history associations to transcribe a large corpus of image-based data from the General Register Office (GRO) records. Our model-driven approach generates the software application from data, workflow and GUI abstract models, ready for deployment.

Historical Civil Registration Record Transcription Using an eXtreme Model Driven Approach

Modelling is considered as a universal approach to define and simplify real-world applications through appropriate abstraction. Model-driven system engineering identifies and integrates appropriate concepts, techniques, and tools which provide important artefacts for interdisciplinary activities. In this paper, we show how we used a model-driven approach to design and improve a Digital Humanities dynamic web application within an interdisciplinary project that enables history students and volunteers of history associations to transcribe a large corpus of image-based data from the General Register Office (GRO) records. Our model-driven approach generates the software application from data, workflow and GUI abstract models, ready for deployment.

Automated Test Selection for Android Apps Based on APK and Activity Classification

Several techniques exist for mobile test automation, from script-based techniques to automated test generation based on GUI models. Most techniques fall short in being adopted extensively by practitioners because of the very costly definition (and maintenance) of test cases. We present a novel testing framework for Android apps that allows a developer to write effective test scripts without having to know the implementation details and the user interface of the app under test. The main goal of the framework is to generate adaptive tests that can be executed on a significant number of apps, or different releases of the same app, without manual editing of the tests. The frameworks consists of: (1) a Test Scripting Language, that allows the tester to write generic test scripts tailored to activity and app categories; (2) a State Graph Modeler, that creates a model of the app’s GUI, identifying activities (i.e., screens) and widgets; (3) an app classifier that determines the type of application under test; (4) an activity classifier that determines the purpose of each screen; (5) a test adapter that executes test scripts that are compatible with the specific app and activity, automatically tailoring the test scripts to the classes of the app and the activities under test. We evaluated empirically the components of our testing framework. The classifiers were able to outperform available approaches in the literature. The developed testing framework was able to correctly adapt high-level test cases to 28 out of 32 applications, and to reduce the LOCs of the test scripts of around 90%. We conclude that machine learning can be fruitfully applied to the creation of high-level, adaptive test cases for Android apps. Our framework is modular in nature and allows expansions through the addition of new commands to be executed on the classified apps and activities.

Cloud-based Simulation Tools for Cloud Testing: A Review

Cloud computing has maintained popularity and attraction in the ICT industry. Many business organizations have embraced the benefits that it offers and many more are still working towards cloud computing. One of the many concerns many organizations and individuals have about cloud computing from its inception is how to test the services and opportunities offered by cloud computing. This concern is as a result of the cost of running experiments on real cloud system and the fact that real infrastructure limits experiments within the size of the infrastructure, thereby making it really difficult to reproduce test. The alternative solution to this concern is the use of cloud simulation tools which offer a cloud environment to test cloud services in a repeated and controlled manner at no cost. Many cloud simulation tools have been developed since the inception of cloud computing. These tools have their strengths and weakness in modelling and simulating cloud system. This paper reviews some simulation tools and compares them in terms of their underlying framework, programming language, GUI, availability, cost modelling, energy modelling, simulation time, federation policy and communication model

Aiding exploratory testing with pruned GUI models

Exploratory testing is a kind of software testing approach that emphasizes tester's experience to maximize the chances to find bugs within a specific time period. It is naturally a GUI-oriented testing activity for GUI-based systems. We propose aiding exploratory testing by providing a GUI model of the region impacted by the most recent internal code changes. We create such a delimited GUI model by pruning an original GUI model, quickly built by static analysis. This pruned GUI model is result of a reachability analysis between GUI elements and internal source code changes. Only related GUI elements are preserved. To illustrate the idea, we consider five GUI applications found in public repositories with varying changes among them. We present experimental data concerning two executions of two exploratory testing sessions: one without using our proposal and another with our proposal. For both testers, our proposal showed coverage gain in experimental data.

Динамический анализ приложений с графическим пользовательским интерфейсом на основе символьного исполнения

In this paper, we explore the possibilities of applying dynamic symbolic execution (or concolic testing) methods to applications with graphical user interfaces. Such applications inherently feature interactive user input processing and multithreaded execution. These features typically decrease the effectiveness of dynamic symbolic execution by increasing the volume of processed code not related to actual application functionality. We present a hybrid approach that combines commonly used GUI test automation methods based on GUI model excavation with dynamic symbolic execution methods to construct test cases for checking internal application logic. We have implemented this approach using two open-source tools - test automation framework GUITAR and Java byte-code static instrumentation framework Coffee Machine. GUI model extracted automatically by GUITAR tool is extended with symbolic traces relevant to application GUI event handlers. Our test generation module for GUITAR combines these symbolic traces into complex queries to be processed by SMT solver. The resulting test cases are valid within automatically extracted GUI structure model and allow to check different execution paths in GUI event handler code. We have checked our hybrid approach on a set of small open-source applications and identified several bugs caused by uncaught exceptions. The paper is concluded with an overview of current limitations and possible improvements of the hybrid approach.

Прикладное применение динамического анализа программ, исполняющихся в интерпретирующих средах

The present-day trends in software engineering include the steady increase of code and design complexity which reinforces the high demand in automated software testing and analysis tools. In this paper, we showcase several dynamic program analysis applications and present our solutions. These applications include memory profiling, automated test generation using dynamic symbolic execution and automated detection of concurrency bugs in multithreaded programs. Our memory profiling tool is designed for Java applications for Android and it is implemented through Android Dalvik VM modification. This approach allowed us to overcome existing Dalvik VM limitations that make existing profiling tools based on dynamic bytecode instrumentation inaccessible. We have successfully applied our tool to several core Android applications - the results provided in the paper outline the effectiveness of the approach. The second solution we discuss in the paper - dynamic symbolic execution for test generation automation - allows us to efficiently generate test scenarios for Java program graphical user interface. The core technologies of the approach include the use of static bytecode instrumentation and automatic GUI model extraction. We implement the approach on top of a user interface test automation framework GUITAR. Finally, we present our approach to automatically identify concurrency bugs in multithreaded Java applications. The approach is based on static bytecode instrumentation for trace generation and employs ThreadSanitizer defect detection tool for identifying bugs.

Building a Model-Based GUI Test Automation System for Mobile Applications

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile applications (apps). This brings new business requirements and demands in mobile software testing, and causes new issues and challenges in mobile test automation. Existing mobile application testing approaches mostly concentrate on GUI-based testing, load and performance testing without considering large-scale concurrent mobile app test automation, and model-based test coverage analysis. In this paper, a mobile hierarchical GUI model is proposed to present mobile operation scenario flows and gesture features in a hierarchical manner, in order to facilitate test dependency analysis in test automation. Mobile app test coverage analysis is performed based on GUI ripping models. The paper also presents a developed system that provides a test automation solution using GUI models. Finally, the paper reports a case study to indicate the feasibility and effectiveness of the proposed approach.

모바일 앱에서의 사용자 행동 모델 기반 GUI 사용성 저해요소 검출 기법

Given the attributes of mobile apps that shorten the time to make purchase decisions while enabling easy purchase cancellations, usability can be regarded to be a highly prioritized quality attribute among the diverse quality attributes that must be provided by mobile apps. With that backdrop, mobile app developers have been making great effort to minimize usability hampering elements that degrade the merchantability of apps in many ways. Most elements that hamper the convenience in use of mobile apps stem from those potential errors that occur when GUIs are designed. In our previous study, we have proposed a technique to analyze the usability of mobile apps using user behavior logs. We proposes a technique to detect usability hampering elements lying dormant in mobile apps` GUI models by expressing user behavior logs with finite state models, combining user behavior models extracted from multiple users, and comparing the combined user behavior model with the expected behavior model on which the designer`s intention is reflected. In addition, to reduce the burden of the repeated test operations that have been conducted by existing developers to detect usability errors, the present paper also proposes a mobile usability error detection automation tool that enables automatic application of the proposed technique. The utility of the proposed technique and tool is being discussed through comparison between the GUI issue reports presented by actual open source app developers and the symptoms detected by the proposed technique.

Industry Monitoring Robot using Arduino Uno with Matlab Interface

In industries, measuring the parameters of machines is still a tedious process which is carried out by some human personnel. This paper mainly focuses on the remedial measures made to reduce human interaction with machines by using Industrial monitoring robot. This robot is hanged over the ropes to measure the parameters of machines in aerial mode. During its travel, the robot collects the parameters from the machines and transmits to control room using ZIG-BEE protocol. The control room interface with the robot is designed using MATLAB GUI model. MATLAB GUI interface shows the parameter readings from the robot and plotted in graph. The robot mainly observes temperature, smoke, toxic gases, Light intensity, defect in manufacturing machines, security purposes using cameras. Any abnormal change in the parameters can be viewed in the graph and remedial measures can be made immediately without any delay, this prevents machine damage to a greater extent. This robot is also used to indicate hazardous conditions like fire, leakage of pipes etc. to the control room using buzzers.

효과적인 모델 기반 안드로이드 GUI 테스팅을 위한 GUI 상태 비교 기법

Graphical user interface testing (GUI testing) techniques have been widely used to test the functionality of Android applications (apps) and to detect faults for verification of the reliability and usability of apps. To adequately test the behaviors of apps, a number of studies on model-based GUI testing techniques have been performed on Android apps. However, the effectiveness of model-based techniques greatly depends on the quality of the GUI model, because model-based GUI testing techniques generate test inputs based on this model. Therefore, in order to improve testing effectiveness in model-based techniques, accurate and efficient GUI model generation has to be achieved using an improved model generation technique with concrete definition of GUI states. For accurate and efficient generation of a GUI model and test inputs, this study suggests a hierarchical GUI state comparison technique and evaluates this technique through comparison with the existing model-based techniques, considering activities as GUI states. Our results show that the proposed technique outperforms existing approaches and has the potential to improve the performance of model-based GUI testing techniques for Android apps.

PSO based Automated Test Coverage Analysis of Event Driven Systems

Graphical User Interface (GUI, pronounced sometimes as gooey as well) was first developed in 1981 and has become an essence for today's computing. A GUI contains graphical objects having certain distinct values which can be used to determine the state of the GUI at any time. Developing organizations always desire to thoroughly test the software to get maximum confidence about its quality, but this requires gigantic effort to test a GUI application due to complexity involve in such applications. This problem has led to automate GUI testing and different techniques have been proposed for automated GUI Testing. Event-flow graph is a fresh breach in the field of automated GUI testing. As control-flow graph, another GUI model represents all possible execution paths in a program; in the same way, event-flow model represents all promising progressions of events that can be executed on the GUI. Another challenging question in software testing is, how much testing is enough? There are few measures that can be used...

A Model-Driven Methodology for Developing Secure Data-Management Applications

We present a novel model-driven methodology for developing secure data-management applications. System developers proceed by modeling three different views of the desired application: its data model, security model, and GUI model. These models formalize respectively the application's data domain, authorization policy, and its graphical interface together with the application's behavior. Afterwards a model-transformation function lifts the policy specified by the security model to the GUI model. This allows a separation of concerns where behavior and security are specified separately, and subsequently combined to generate a security-aware GUI model. Finally, a code generator generates a multi-tier application, along with all support for access control, from the security-aware GUI model. We report on applications built using our approach and the associated tool.

Regressive Augmentation: Investigating Ubicomp’s Romantic Promises

Regressive Augmentation: Investigating Ubicomp’s Romantic Promises

Fuzzy Cognitive Maps Based Election Results Prediction System

Prediction and forecast are common words in the area of election. It can also be related with word “opinion poll”. Although according to dictionary the meaning/definition of prediction is limited but practically prediction of election cannot be calculated under limited region. In general term, everybody is chipped with prediction. This relation may be direct or indirect. Prediction of weather, prediction about share market etc are few examples of prediction. Because of this, n numbers of predictions can be calculated. Are these accurate as compared to actual result? This is still a question having no suitable answer. To predict the result from election is again a wide concept. Majority of tools and models are there which are used to predict the result but still the area of social science or political science is not up to that level where others are. Due to its vague and imprecision, it is quite complex. The objective of this research is to design and develop a simple GUI model based upon FCM which can be used to calculate the prediction in the term of winning chances of a candidate before the actual result. The concept of fuzzy logic along with fuzzy cognitive maps (FCM) is used to determine the relation between various input parameters. An accurate and simple GUI is designed for this proposed model.

Parametric Modeling Technology for Transmission Line Towers

Transmission towers have characteristics of similarities and succession; therefore parametric modeling ideas can be used in simulation study on the tower. Using CSG method, characteristics parameters extraction and the 2Z2E3 double-loop linear tower, the paper suggests parametric modeling analyzing the 2Z2E3 double-loop linear tower. Finally, its verified that the feasibility of parametric modeling through the modal analysis comparison between parametric model and GUI model in ANSYS.

Using Mutation to Enhance GUI Testing Coverage

Mutation testing improves a system's bug-detection capability. It also helps improve coverage by exposing software or code areas that other types of testing might not expose. However, the value of mutation testing is often bypassed because it consumes extra resources from already limited ones. The author presents an automatic technique to generate valid and mutant test cases. In traditional mutation testing, one or more parameters in the specification or the code are changed, and the technique finds the test cases that can detect those mutations. In the author's approach, the test cases generated by a GUI model are mutated and the mutants are then applied the model to test its capability to kill the mutant test cases by rejecting them. A robust model is expected to differentiate between a valid and invalid event sequence. The author also describes an automatic execution and verification technique to evaluate the test cases rejected by the GUI model and to calculate coverage based on their number relative to the total number of test cases. Experimental results indicate the value of this mutation process and its potential for improving software test coverage automatically.