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

Visualization of Radiation For Optimized Design of Automotive Antennas

A MATLAB-based tool is introduced, which is used to help design networks of on-glass automobile antennas. It is used to simulate the radiation patterns of on-glass antennas and view and compare the resulting data using flexible, user-defined functions.AGC AmericaNo embarg

A Fuzzy Rule-Based System to Predict Energy Consumption of Genetic Programming Algorithms

In recent years, the energy-awareness has become one of the most interesting areas in our environmentally conscious society. Algorithm designers have been part of this, particularly when dealing with networked devices and, mainly, when handheld ones are involved. Although studies in this area has increased, not many of them have focused on Evolutionary Algorithms. To the best of our knowledge, few attempts have been performed before for modeling their energy consumption considering different execution devices. In this work, we propose a fuzzy rulebased system to predict energy comsumption of a kind of Evolutionary Algorithm, Genetic Prohramming, given the device in wich it will be executed, its main parameters, and a measurement of the difficulty of the problem addressed. Experimental results performed show that the proposed model can predict energy consumption with very low error values.We acknowledge support from Spanish Ministry of Economy and Competitiveness under projects TIN2014-56494-C4-[1,2,3]-P and TIN2017-85727-C4- [2,4]-P, Regional Government of Extremadura, Department of Commerce and Economy, conceded by the European Regional Development Fund, a way to build Europe, under the project IB16035, and Junta de Extremadura FEDER, projects GR15068 and GR15130

Guiding Random Graphical and Natural User Interface Testing Through Domain Knowledge

Users have access to a diverse set of interfaces that can be used to interact with software. Tools exist for automatically generating test data for an application, but the data required by each user interface is complex. Generating realistic data similar to that of a user is difficult. The environment which an application is running inside may also limit the data available, or updates to an operating system can break support for tools that generate test data. Consequently, applications exist for which there are no automated methods of generating test data similar to that which a user would provide through real usage of a user interface. With no automated method of generating data, the cost of testing increases and there is an increased chance of bugs being released into production code. In this thesis, we investigate techniques which aim to mimic users, observing how stored user interactions can be split to generate data targeted at specific states of an application, or to generate different subareas of the data structure provided by a user interface. To reduce the cost of gathering and labelling graphical user interface data, we look at generating randomised screen shots of applications, which can be automatically labelled and used in the training stage of a machine learning model. These trained models could guide a randomised approach at generating tests, achieving a significantly higher branch coverage than an unguided random approach. However, for natural user interfaces, which allow interaction through body tracking, we could not learn such a model through generated data. We find that models derived from real user data can generate tests with a significantly higher branch coverage than a purely random tester for both natural and graphical user interfaces. Our approaches use no feedback from an application during test generation. Consequently, the models are “generating data in the dark”. Despite this, these models can still generate tests with a higher coverage than random testing, but there may be a benefit to inferring the current state of an application and using this to guide data generation

Using Software Testing Techniques to Infer Biological Models

Years of research in software testing has given us novel ways to reason about and test the behavior of complex software systems that contain hundreds of thousands of lines of code. Many of these techniques have been inspired by nature such as genetic algorithms, swarm intelligence, and ant colony optimization. However, they use a unidirectional analogy – taking from nature without giving back. In this thesis we invert this view and ask if we can utilize techniques from testing and modeling of highly-configurable software systems to aid in the emerging field of systems biology which aims to model and predict the behavior of biological organisms. Like configurable systems, the underlying source code (metabolic model) contains both common and variable code elements (reactions) that are executed only under particular configurations (environmental conditions), and these directly impact an organism’s observable behavior. We propose the use of sampling, classification, and modeling techniques commonly used in software testing and combine them into a process called BioSIMP which can lead to simplified models and biological predictions. We perform two case studies, the first of which explores and evaluates different classification techniques to infer influential factors in microbial organisms. We then compare several sampling methods to limit the number of experiments required in the laboratory. We show that we can reduce testing by more than two thirds without negatively impacting the quality of our models. Finally, we perform an end-to-end case study on BioSIMP using both laboratory and simulation data and show that we can find influencing environmental factors in two microbial organisms, some of which were previously unknown to biologists. Our findings suggest that the configurable-software analogy holds, and we can identify the variable and common regions of reactions that change with respect to the environment. Advisor: Myra B. Cohe

Metaheuristic Optimization Frameworks: a Survey and Benchmarking

This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include the different metaheuristic techniques covered, mechanisms for solution encoding, constraint handling, neighborhood specification, hybridization, parallel and distributed computation, software engineering best practices, documentation and user interface, etc. A metric has been defined for each feature so that the scores obtained by a framework are averaged within each group of features, leading to a final average score for each framework. Out of 33 frameworks ten have been selected from the literature using well-defined filtering criteria, and the results of the comparison are analyzed with the aim of identifying improvement areas and gaps in specific frameworks and the whole set. Generally speaking, a significant lack of support has been found for hyper-heuristics, and parallel and distributed computing capabilities. It is also desirable to have a wider implementation of some Software Engineering best practices. Finally, a wider support for some metaheuristics and hybridization capabilities is needed

Misconfiguration in Firewalls and Network Access Controls: Literature Review

Firewalls and network access controls play important roles in security control and protection. Those firewalls may create an incorrect sense or state of protection if they are improperly configured. One of the major configuration problems in firewalls is related to misconfiguration in the access control roles added to the firewall that will control network traffic. In this paper, we evaluated recent research trends and open challenges related to firewalls and access controls in general and misconfiguration problems in particular. With the recent advances in next-generation (NG) firewalls, firewall roles can be auto-generated based on networks and threats. Nonetheless, and due to the large number of roles in any medium to large networks, roles’ misconfiguration may occur for several reasons and will impact the performance of the firewall and overall network and protection efficiency

Sapienz: Multi-objective automated testing for android applications

We introduce Sapienz, an approach to Android testing that uses multi-objective search-based testing to automatically explore and optimise test sequences, minimising length, while simultaneously maximising coverage and fault revelation. Sapienz combines random fuzzing, systematic and search-based exploration, exploiting seeding and multi-level instrumentation. Sapienz significantly outperforms (with large effect size) both the state-of-the-art technique Dynodroid and the widely-used tool, Android Monkey, in 7/10 experiments for coverage, 7/10 for fault detection and 10/10 for fault-revealing sequence length. When applied to the top 1, 000 Google Play apps, Sapienz found 558 unique, previously unknown crashes. So far we have managed to make contact with the developers of 27 crashing apps. Of these, 14 have confirmed that the crashes are caused by real faults. Of those 14, six already have developer-confirmed fixes

X-Machines for Agent-Based Modeling

This book discusses various aspects of agent-based modeling and simulation using FLAME (Flexible Large-scale Agent-Based Modeling Environment) which is a popular agent-based modeling environment that enables automatic parallelization of models. Along with a focus on the software engineering principles in building agent-based models, the book comprehensively discusses how models can be written for various domains including biology, economics and social networks. The book also includes examples to guide readers on how to write their own models