Automated Functional Testing based on the Navigation of Web Applications

Web applications are becoming more and more complex. Testing such applications is an intricate hard and time-consuming activity. Therefore, testing is often poorly performed or skipped by practitioners. Test automation can help to avoid this situation. Hence, this paper presents a novel approach to perform automated software testing for web applications based on its navigation. On the one hand, web navigation is the process of traversing a web application using a browser. On the other hand, functional requirements are actions that an application must do. Therefore, the evaluation of the correct navigation of web applications results in the assessment of the specified functional requirements. The proposed method to perform the automation is done in four levels: test case generation, test data derivation, test case execution, and test case reporting. This method is driven by three kinds of inputs: i) UML models; ii) Selenium scripts; iii) XML files. We have implemented our approach in an open-source testing framework named Automatic Testing Platform. The validation of this work has been carried out by means of a case study, in which the target is a real invoice management system developed using a model-driven approach.Comment: In Proceedings WWV 2011, arXiv:1108.208

ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.Comment: Published in the ACM Conference on Human Factors in Computing Systems (CHI), 2019, Glasgow, Scotland U

Automated Test Input Generation for Android: Are We There Yet?

Mobile applications, often simply called "apps", are increasingly widespread, and we use them daily to perform a number of activities. Like all software, apps must be adequately tested to gain confidence that they behave correctly. Therefore, in recent years, researchers and practitioners alike have begun to investigate ways to automate apps testing. In particular, because of Android's open source nature and its large share of the market, a great deal of research has been performed on input generation techniques for apps that run on the Android operating systems. At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test, and the specific heuristics they use. To better understand the strengths and weaknesses of these existing approaches, and get general insight on ways they could be made more effective, in this paper we perform a thorough comparison of the main existing test input generation tools for Android. In our comparison, we evaluate the effectiveness of these tools, and their corresponding techniques, according to four metrics: code coverage, ability to detect faults, ability to work on multiple platforms, and ease of use. Our results provide a clear picture of the state of the art in input generation for Android apps and identify future research directions that, if suitably investigated, could lead to more effective and efficient testing tools for Android

The Seedling Sanctuary: Automated Cold Frame for Gardner Elementary

The purpose of this report is to provide the details of the Seedling Sanctuary, a mechanical engineering senior design project. The project in question is an automated cold frame designed specifically for Gardner Academy, a local elementary school in San Jose. A cold frame is a miniature greenhouse that opens like a chest and is made from clear plastic. Automated ventilation and watering systems create a microclimate within this greenhouse structure to create the ideal growing conditions for seeds. The main purposes of the cold frame are to lengthen the growing season, be maintenance free, and enhance garden education. From testing, the project goals were verified to have been achieved through several performance metrics. First, the system’s ability to lengthen the growing season is dependent on germinating seedlings that can be planted earlier in the season. The automated system maintained the seedlings at the proper soil moisture levels to grow. The system also implemented passive temperature control systems to maintain the plants in ideal conditions. With the ventilation and thermal mass, the system is able to be cooler at the hottest times of day and warmer at night than unprotected plants. The system has also successfully automated the care of the seedlings, achieving our goal of being maintenance free. Finally, the enhancement of garden education was incorporated through community engagement with the design and building of the cold frame, as well as the Bluetooth application which will be used in the school curriculum