Implementing Test Automation with Selenium WebDriver

Many software programs, such as applications for designing, modeling, simulating, and analyzing systems, are now commonly available as web-based applications. The testing of such sophisticated web applications is highly challenging and can be extremely tedious and error-prone if done manually. Recently automation tools have become increasingly used for testing web-based applications, as they minimize human involvement and repetitive work. For this problem report project, we have built and implemented an automation testing framework for web applications. The project specifically uses a tool called Selenium WebDriver, which has been used to develop the testing framework. By using this framework, testers may quickly and effectively write their test cases. The benefits of Selenium WebDriver include that it does not require in-depth research and training by testers, and due to the framework\u27s ability to take screenshots, it provides a useful way for developers to study their code. The framework relies on the Chrome web browser, along with Java running in Eclipse, to provide a user-friendly interface for constructing and running test suites. To validate the testing framework, we performed a case study involving NanoHub (nanoHUB.org), which is a well-known platform that provides valuable resources for those involved in nanotechnology research and education. NanoHub serves as an open-access repository for a wide range of tools, simulations, and information related to nanoscale science and engineering, and it is designed particularly to model and simulate electronic systems and nanoscale phenomena. Testing a website such as NanoHub.org typically encompasses a blend of functional testing, usability testing, and performance testing. Based on the results of this testing, several observations are made about the testing framework in general, and its application to NanoHub in particular. The comprehensive testing approach documented in this report is aimed at ensuring the platform functions as intended, provides a user-friendly experience, and delivers optimal performance. This testing is particularly crucial when dealing with tools and simulations related to electronic systems

Predicting Pilot Behavior in Medium Scale Scenarios Using Game Theory and Reinforcement Learning

Effective automation is critical in achieving the capacity and safety goals of the Next Generation Air Traffic System. Unfortunately creating integration and validation tools for such automation is difficult as the interactions between automation and their human counterparts is complex and unpredictable. This validation becomes even more difficult as we integrate wide-reaching technologies that affect the behavior of different decision makers in the system such as pilots, controllers and airlines. While overt short-term behavior changes can be explicitly modeled with traditional agent modeling systems, subtle behavior changes caused by the integration of new technologies may snowball into larger problems and be very hard to detect. To overcome these obstacles, we show how integration of new technologies can be validated by learning behavior models based on goals. In this framework, human participants are not modeled explicitly. Instead, their goals are modeled and through reinforcement learning their actions are predicted. The main advantage to this approach is that modeling is done within the context of the entire system allowing for accurate modeling of all participants as they interact as a whole. In addition such an approach allows for efficient trade studies and feasibility testing on a wide range of automation scenarios. The goal of this paper is to test that such an approach is feasible. To do this we implement this approach using a simple discrete-state learning system on a scenario where 50 aircraft need to self-navigate using Automatic Dependent Surveillance-Broadcast (ADS-B) information. In this scenario, we show how the approach can be used to predict the ability of pilots to adequately balance aircraft separation and fly efficient paths. We present results with several levels of complexity and airspace congestion

Modeling and Simulation of Biological Systems through Electronic Design Automation techniques

Modeling and simulation of biological systems is a key requirement for integrating invitro and in-vivo experimental data. In-silico simulation allows testing different experimental conditions, thus helping in the discovery of the dynamics that regulate the system. These dynamics include errors in the cellular information processing that are responsible for diseases such as cancer, autoimmunity, and diabetes as well as drug effects to the system (Gonalves, 2013). In this context, modeling approaches can be classified into two categories: quantitative and qualitative models. Quantitative modeling allows for a natural representation of molecular and gene networks and provides the most precise prediction. Nevertheless, the lack of kinetic data (and of quantitative data in general) hampers its use for many situations (Le Novere, 2015). In contrast, qualitative models simplify the biological reality and are often able to reproduce the system behavior. They cannot describe actual concentration levels nor realistic time scales. As a consequence, they cannot be used to explain and predict the outcome of biological experiments that yield quantitative data. However, given a biological network consisting of input (e.g., receptors), intermediate, and output (e.g., transcription factors) signals, they allow studying the input-output relationships through discrete simulation (Samaga, 2013). Boolean models are gaining an increasing interest in reproducing dynamic behaviors, understanding processes, and predicting emerging properties of cellular signaling networks through in-silico experiments. They are emerging as a valid alternative to the quantitative approaches (i.e., based on ordinary differential equations) for exploratory modeling when little is known about reaction kinetics or equilibrium constants in the context of gene expression or signaling. Even though several approaches and software have been recently proposed for logic modeling of biological systems, they are limited to specific contexts and they lack of automation in analyzing biological properties such as complex attractors, and molecule vulnerability. This thesis proposes a platform based on Electronic Design Automation (EDA) technologies for qualitative modeling and simulation of Biological Systems. It aims at overtaking limitations that affect the most recent qualitative tools