Dynamically Testing Graphical User Interfaces

Software test generation for GUIs is a hard problem. The goal of this thesis is to investigate different methods for dynamically generating tests for GUIs. We introduce the concept of an event-pair graph, which is used to represent and measure test suites, and show how it can be used to generate tests and measure GUI coverage. Before we can begin generating tests, we first want to determine which is better: a small test suite with a few long tests or a large test suite with many short tests. Therefore, we designed and conducted a study to determine which is more effective. We found that moderate to long tests perform better than short tests. We then move on to discuss seven test generation algorithms. Two are based on random selection, two are based on greedy selection, one is based on Q-Learning, and the last two are based on ant colony optimization. We conducted a study in order to compare the performance of each algorithm. We measured code coverage, GUI coverage, time to run, and faults found. The results show that the greedy algorithms performed the best. Finally, we conducted a study in order to determine if any of the GUI coverage metrics can be used to predict code coverage, and we conducted a study to determine if any of the coverage metrics can be used to predict the faults found. The results show that event pairs are good at predicting code coverage, and that predicting faults is difficult

A Regression Test Selection Technique for Graphical User Interfaces

Regression testing is a quality control measure to ensure that the newly modified part of the software still complies with its specified requirements and that the unmodified part has not been affected by the maintenance activity. Regression testing is an important and expensive activity during the software maintenance process and its purpose is to ensure quality and reliability in modified software. Regression testing selection techniques are focused on the reusability of existing test suites for a modified program from a previous version. Many regression testing selection techniques have been approached for conventional and object-oriented software. There is little discussion about those techniques to be applied for the Graphical User Interfaces (GUIs). This thesis addresses the gap. GUIs have characteristics different from traditional software, and the conventional testing techniques do not directly apply to GUIs. Unlike most previous techniques for selective retest, this thesis focuses on developing an event driven regression testing selection technique for GUIs. It defines an event dependence graph (EDG) to identify the interaction and relationship of the events within GUI components, develops an algorithm to construct the EDG for GUIs, and presents the GUI modeling structure and its selection retest technique. An algorithm is given to determine and generate a modified test suite automatically for GUI based on its original version. Experiments are presented on an implementation of this solution and discusses newly found challenges when applied to an established GUI application. Finally, feasibility and future areas of research are addressed on the findings during the implementation of the solution

Development of wireless-based low-cost current controlled stimulator for patients with spinal cord injuries

A spinal cord injury (SCI) has a severe impact on human life in general as well as on the physical status and condition. The use of electrical signals to restore the function of paralyzed muscles is called functional electrical stimulation (FES). FES is a promising way to restore mobility to SCI by applying low-level electrical current to the paralyzed muscles so as to enhance that person’s ability to function and live independently. However, due to the limited number of commercially available FES assisted exerciser systems and their rather high cost, the conventional devices are unaffordable for most peoples. It also inconvenient because of wired based system that creates a limitation in performing exercise. Thus, this project is concerned with the development of low-cost current controlled stimulator mainly for the paraplegic subjects. The developed device should be based on a microcontroller, wireless based system using Zigbee module, voltage-to-current converter circuit and should produce proper monophasic and biphasic current pulses, pulse trains, arbitrary current waveforms, and a trigger output for FES applications. The performances of the device will be assessed through simulation study and validated through experimental work. This device will be developed as in the new technique of the stimulator development with low cost and one of the contributing factors in Rehabilitation Engineering for patients with SCI

Development of wireless-based low-cost current controlled stimulator for patients with spinal cord injuries

A spinal cord injury (SCI) has a severe impact on human life in general as well as on the physical status and condition. The use of electrical signals to restore the function of paralyzed muscles is called functional electrical stimulation (FES). FES is a promising way to restore mobility to SCI by applying low-level electrical current to the paralyzed muscles so as to enhance that person’s ability to function and live independently. However, due to the limited number of commercially available FES assisted exerciser systems and their rather high cost, the conventional devices are unaffordable for most peoples. It also inconvenient because of wired based system that creates a limitation in performing exercise. Thus, this project is concerned with the development of low-cost current controlled stimulator mainly for the paraplegic subjects. The developed device should be based on a microcontroller, wireless based system using Zigbee module, voltage-to-current converter circuit and should produce proper monophasic and biphasic current pulses, pulse trains, arbitrary current waveforms, and a trigger output for FES applications. The performances of the device will be assessed through simulation study and validated through experimental work. This device will be developed as in the new technique of the stimulator development with low cost and one of the contributing factors in Rehabilitation Engineering for patients with SCI

Analysing Java Identifier Names

Identifier names are the principal means of recording and communicating ideas in source code and are a significant source of information for software developers and maintainers, and the tools that support their work. This research aims to increase understanding of identifier name content types - words, abbreviations, etc. - and phrasal structures - noun phrases, verb phrases, etc. - by improving techniques for the analysis of identifier names. The techniques and knowledge acquired can be applied to improve program comprehension tools that support internal code quality, concept location, traceability and model extraction. Previous detailed investigations of identifier names have focused on method names, and the content and structure of Java class and reference (field, parameter, and variable) names are less well understood. I developed improved algorithms to tokenise names, and trained part-of-speech tagger models on identifier names to support the analysis of class and reference names in a corpus of 60 open source Java projects. I confirm that developers structure the majority of names according to identifier naming conventions, and use phrasal structures reported in the literature. I also show that developers use a wider variety of content types and phrasal structures than previously understood. Unusually structured class names are largely project-specific naming conventions, but could indicate design issues. Analysis of phrasal reference names showed that developers most often use the phrasal structures described in the literature and used to support the extraction of information from names, but also choose unexpected phrasal structures, and complex, multi-phrasal, names. Using Nominal - software I created to evaluate adherence to naming conventions - I found developers tend to follow naming conventions, but that adherence to published conventions varies between projects because developers also establish new conventions for the use of typography, content types and phrasal structure to support their work: particularly to distinguish the roles of Java field names

Deep Reinforcement Learning Driven Applications Testing

Applications have become indispensable in our lives, and ensuring their correctness is now a critical issue. Automatic system test case generation can significantly improve the testing process for these applications, which has recently motivated researchers to work on this problem, defining various approaches. However, most state-of-the-art approaches automatically generate test cases leveraging symbolic execution or random exploration techniques. This led to techniques that lose efficiency when dealing with an increasing number of program constraints and become inapplicable when conditions are too challenging to solve or even to formulate. This Ph.D. thesis proposes addressing current techniques' limitations by exploiting Deep Reinforcement Learning. Deep Reinforcement Learning (Deep RL) is a machine learning technique that does not require a labeled training set as input since the learning process is guided by the positive or negative reward experienced during the tentative execution of a task. Hence, it can be used to dynamically learn how to build a test suite based on the feedback obtained during past successful or unsuccessful attempts. This dissertation presents three novel techniques that exploit this intuition: ARES, RONIN, and IFRIT. Since functional testing and security testing are complementary, this Ph.D. thesis explores both testing techniques using the same approach for test cases generation. ARES is a Deep RL approach for functional testing of Android apps. RONIN addresses the issue of generating exploits for a subset of Android ICC vulnerabilities. Subsequently, to better expose the bugs discovered by previous techniques, this thesis presents IFRIT, a focused testing approach capable of increasing the number of test cases that can reach a specific target (i.e., a precise section or statement of an application) and their diversity. IFRIT has the ultimate goal of exposing faults affecting the given program point

Why Creating Web Page Objects Manually if It Can Be Done Automatically?

Page Object is a design pattern aimed at making web test scripts more readable, robust and maintainable. The effort to manually create the page objects needed for a web application may be substantial and unfortunately existing tools do not help web developers in such task.In this paper we present APOGEN, a tool for the automatic generation of page objects for web applications. Our tool automatically derives a testing model by reverse engineering the target web application and uses a combination of dynamic and static analysis to generate Java page objects for the popular Selenium WebDriver framework. Our preliminary evaluation shows that it is possible to use around 3/4 of the automatic page object methods as they are, while the remaining 1/4 need only minor modifications

Automating Software Development for Mobile Computing Platforms

Mobile devices such as smartphones and tablets have become ubiquitous in today\u27s computing landscape. These devices have ushered in entirely new populations of users, and mobile operating systems are now outpacing more traditional desktop systems in terms of market share. The applications that run on these mobile devices (often referred to as apps ) have become a primary means of computing for millions of users and, as such, have garnered immense developer interest. These apps allow for unique, personal software experiences through touch-based UIs and a complex assortment of sensors. However, designing and implementing high quality mobile apps can be a difficult process. This is primarily due to challenges unique to mobile development including change-prone APIs and platform fragmentation, just to name a few. in this dissertation we develop techniques that aid developers in overcoming these challenges by automating and improving current software design and testing practices for mobile apps. More specifically, we first introduce a technique, called Gvt, that improves the quality of graphical user interfaces (GUIs) for mobile apps by automatically detecting instances where a GUI was not implemented to its intended specifications. Gvt does this by constructing hierarchal models of mobile GUIs from metadata associated with both graphical mock-ups (i.e., created by designers using photo-editing software) and running instances of the GUI from the corresponding implementation. Second, we develop an approach that completely automates prototyping of GUIs for mobile apps. This approach, called ReDraw, is able to transform an image of a mobile app GUI into runnable code by detecting discrete GUI-components using computer vision techniques, classifying these components into proper functional categories (e.g., button, dropdown menu) using a Convolutional Neural Network (CNN), and assembling these components into realistic code. Finally, we design a novel approach for automated testing of mobile apps, called CrashScope, that explores a given android app using systematic input generation with the intrinsic goal of triggering crashes. The GUI-based input generation engine is driven by a combination of static and dynamic analyses that create a model of an app\u27s GUI and targets common, empirically derived root causes of crashes in android apps. We illustrate that the techniques presented in this dissertation represent significant advancements in mobile development processes through a series of empirical investigations, user studies, and industrial case studies that demonstrate the effectiveness of these approaches and the benefit they provide developers