This dissertation introduces a hybrid strategy for automated testing of Android applications that combines reinforcement learning and test suite prioritization. These approaches aim to improve the effectiveness of the testing process by employing reinforcement learning algorithms, namely Q-learning and SARSA (State-Action-Reward-State-Action), for automated test case generation. The studies provide compelling evidence that reinforcement learning techniques hold great potential in generating test cases that consistently achieve high code coverage; however, the generated test cases may not always be in the optimal order. In this study, novel test case prioritization methods are developed, leveraging pairwise event interactions coverage, application state coverage, and application activity coverage, so as to optimize the rates of code coverage specifically for SARSA-generated test cases. Additionally, test suite prioritization techniques are introduced based on UI element coverage, test case cost, and test case complexity to further enhance the ordering of SARSA-generated test cases. Empirical investigations demonstrate that applying the proposed test suite prioritization techniques to the test suites generated by the reinforcement learning algorithm SARSA improved the rates of code coverage over original orderings and random orderings of test cases.

<p dir="ltr">A Human-Machine Interaction (HMI) refers to a mechanism to support the direct interactions of humans and machines with the objective for the synthesis of machine intelligence and autonomy. The demand to advance in this field of study for intelligence controls is continuously growing. Brain-Computer Interface (BCI) is one type of HMIs that utilizes a human brain to enable direct communication of the human subject with a machine. This technology is widely explored in different fields to control external devices using brain signals.</p><p dir="ltr">This thesis is driven by two key observations. The first one is the limited number of Degrees of Freedom (DoF) that existing BCI controls can control in an external device; it becomes necessary to assess the controllability when choosing a control instrument. The second one is the differences of decision spaces of human and machine when both of them try to control an external device. To fill the gaps in these two aspects, there is a need to design an additional functional module that is able to translate the commands issued by human into high-frequency control commands that can be understood by machines. These two aspects has not been investigated thoroughly in literatures.</p><p dir="ltr">This study focuses on training, detecting, and using humans’ intents to control intelligent machines. It uses brain signals which will be trained and detected in form of Electroencephalography (EEG), brain signals will be used to extract and classify human intents. A selected instrument, Emotiv Epoc X, is used for pattern training and recognition based on its controllability and features among other instruments. A functional module is then developed to bridge the gap of frequency differences between human intents and motion commands of machine. A selected robot, TinkerKit Braccio, is then used to illustrate the feasibility of the developed module through fully controlling the robotic arm using human’s intents solely.</p><p dir="ltr">Multiple experiments were done on the prototyped system to prove the feasibility of the proposed model. The accuracy to send each command, and hence the accuracy of the system to extract each intent, exceeded 75%. Then, the feasibility of the proposed model was also tested through controlling the robot to follow pre-defined paths, which was obtained through designing a Graphical-User Interface (GUI). The accuracy of each experiment exceeded 90%, which validated the feasibility of the proposed control model.</p>

Mobile applications play an important role in the dissemination of computing and information resources. They are often used in domains such as mobile banking, e-commerce, and health monitoring. Cost-effective testing techniques in these domains are critical. This dissertation contributes novel techniques for automatic construction of mobile application test suites. In particular, this work provides solutions that focus on the prohibitively large number of possible event sequences that must be sampled in GUI-based mobile applications. This work makes three major contributions: (1) an automated GUI testing tool, Autodroid, that implements a novel online approach to automatic construction of Android application test suites (2) probabilistic and combinatorial-based algorithms that systematically sample the input space of Android applications to generate test suites with GUI/context events and (3) empirical studies to evaluate the cost-effectiveness of our techniques on real-world Android applications. Our experiments show that our techniques achieve better code coverage and event coverage compared to random test generation. We demonstrate that our techniques are useful for automatic construction of Android application test suites in the absence of source code and preexisting abstract models of an Application Under Test (AUT). The insights derived from our empirical studies provide guidance to researchers and practitioners involved in the development of automated GUI testing tools for Android applications.

The industrial production of semiconductor assemblies is subject to high requirements. As a result, several tests are needed in terms of component quality. In the long run, manual quality assurance (QA) is often connected with higher expenditures. Using a technique based on machine learning, some of these tests may be carried out automatically. Deep neural networks (NN) have shown to be very effective in a diverse range of computer vision applications. Especially convolutional neural networks (CNN), which belong to a subset of NN, are an effective tool for image classification. Deep NNs have the disadvantage of requiring a significant quantity of training data to reach excellent performance. When the dataset is too small a phenomenon known as overfitting can occur. Massive amounts of data cannot be supplied in certain contexts, such as the production of semiconductors. This is especially true given the relatively low number of rejected components in this field. In order to prevent overfitting, a variety of image augmentation methods may be used to the process of artificially creating training images. However, many of those methods cannot be used in certain fields due to their inapplicability. For this thesis, Infineon Technologies AG provided the images of a semiconductor component generated by an ultrasonic microscope. The images can be categorized as having a sufficient number of good and a minority of rejected components, with good components being defined as components that have been deemed to have passed quality control and rejected components being components that contain a defect and did not pass quality control. The accomplishment of the project, the efficacy with which it is carried out, and its level of quality may be dependent on a number of factors; however, selecting the appropriate tools is one of the most important of these factors because it enables significant time and resource savings while also producing the best results. We demonstrate a data augmentation graphical user interface (GUI) tool that has been widely used in the domain of image processing. Using this method, the dataset size has been increased while maintaining the accuracy-time trade-off and optimizing the robustness of deep learning models. The purpose of this work is to develop a user-friendly tool that incorporates traditional, advanced, and smart data augmentation, image processing, and machine learning (ML) approaches. More specifically, the technique mainly uses are zooming, rotation, flipping, cropping, GAN, fusion, histogram matching, autoencoder, image restoration, compression etc. This focuses on implementing and designing a MATLAB GUI for data augmentation and ML models. The thesis was carried out for the Infineon Technologies AG in order to address a challenge that all semiconductor industries experience. The key objective is not only to create an easy- to-use GUI, but also to ensure that its users do not need advanced technical experiences to operate it. This GUI may run on its own as a standalone application. Which may be implemented everywhere for the purposes of data augmentation and classification. The objective is to streamline the working process and make it easy to complete the Quality assurance job even for those who are not familiar with data augmentation, machine learning, or MATLAB. In addition, research will investigate the benefits of data augmentation and image processing, as well as the possibility that these factors might contribute to an improvement in the accuracy of AI models.

Mobile applications are growing in popularity and pose new problems in the area of software testing. In particular, mobile applications heavily depend upon user interactions and a dynamically changing environment of system events. In this thesis, we focus on user-driven events and use Q-learning, a reinforcement machine learning algorithm, to generate tests for Android applications under test (AUT). We implement a framework that automates the generation of GUI test cases by using our Q-learning approach and compare it to a uniform random (UR) implementation. A novel feature of our approach is that we generate user-driven event sequences through the GUI, without the source code or the model of the AUT. Hence, considerable amount of cost and time are saved by avoiding the need for model generation for generating the tests. Our results show that the systematic path exploration used by Q-learning results in higher average code coverage in comparison to the uniform random approach.

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Nowadays, smartphones are considered an essential part of our everyday lives. However, as the use of them increases so does their presence in vehicle environments. Prior studies have found that interacting with mobile devices while driving can cause cognitive, manual, and visual distractions. These distractions have a significant effect on driving performance and can result in serious accidents. As a solution to this, some applications, such as Spotify, has taken a different approach by developing a simplified version of their graphica linterface that is specifically designed for driving.The aim of this study is to examine what impacts simplified graphical interfaces has on the user experience for car drivers, focusing on Spotify and their car mode. In order to examine this, an online survey and a feature analysis was conducted. The result of this study shows that Spotify's car mode is missing a lot of features that are important to the users, causing them not to use it. These results highlight the challenge of providing all features that are expected by the users while creating a simpler interface.

Paperboard is produced in a large and complex process where many parameters can affect its quality. The common methods of analysing paperboard quality are through traversing point-like sensors that only measure a very small fraction of the produced paperboard. Holmen Iggesund Paperboard has installed an IR-camera that measures the entire paperboard web and is looking for a tool to assess paperboard quality variation based on these measurements. The purpose of this thesis work is to develop such a tool. This was done through developing a user-friendly computer application (app) IR-measurement Analysis (IRMA). The app is designed to help the process engineers of Iggesunds mill assess the paperboard variability, which strongly correlates with its quality. The quality improvements of paperboard mainly concern eliminating irregularities in thickness, grammage, and moisture. In IRMA, theuser can prepare data, perform statistical analyses, execute a frequency analysis through Fast Fourier Transform and then compare the frequency peaks with the frequencies of possible sources for periodic process variation, for example rolls. Correlating frequency peaks and roll frequencies can indicate issues with rolls and make process engineers at the mill aware of maintenance needs. IRMA have been tested at the mill and these tests have indicated that IRMA can be beneficial in the process engineer’s daily quality control measures. Furthermore, a manual has been written and the process engineers will be thoroughly educated in the use of IRMA.

I takt med ökad användning av olika slags informationssystem växer även behovet av att testa mjukvaran, vilket innebär att allt fler vill komma igång med testautomation. Denna studie syftar till att beskriva hur man går tillväga för att ta första steget mot testautomationen med hjälp av Robot Framework, samt betona vikten av kravspecifikationer. Studien är en Design Science Research (designbaserad forskning) som löser ett verkligt problem på ett verkligt företag som i dagsläget anställer en hel avdelning av manuella testare och resulterar i en artefakt vilkens fullständig arkitektur redovisas i denna rapport. Problemet går ut på att automatisera ett testflöde, det vill säga en samling av tester som utförs manuellt. Testflödet behandlar den mest komplexa komponenten i hela systemet och tar en hel arbetsdag för manuella testare. Efter utförlig analys och automation förkortas testningstiden till tjugo minuter. Slutprodukten är en enkel applikation som guidar användaren igenom testet där användaren får välja vilka tester ska exekveras, samt sparar exekveringsresultat bestående av loggfiler och (om så önskats) en skärminspelning i en zip-fil. / As the usage of various information systems increases, so does the need to test software, meaning that more companies are interested in getting started with test automation. This study aims to describe how to proceed in order to take the first step towards test automation using Robot Framework, as well as to emphasize the importance of requirement specifications. The study is a Design Science Research, which is solving a real-life problem in a real company that currently employs a whole department of manual testers and is leading to the development of an artifact. The complete architecture of the artifact is disclosed in this report. The problem involves automating a test flow, that is, a collection of tests that are executed manually. The test is carried out on the most complex component in the entire system and takes a whole workday for manual testers. After thorough analysis and automation, the testing time is shortened to twenty minutes. The end product is a simple application that guides the user through the test where the user gets to choose which tests shall be executed, and saves the execution results consisting of log files and (if so wished) a screen recording in a zip file.

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