Testing Basado en la Busqueda en TESTAR

[ES] Las interfaces gráficas de usuario (IGU) constituyen un punto vital para testear una aplicación. Para ello se han desarrollado diversas herramientas automáticas, que, en su mayoría, utilizan algoritmos en los que las acciones a ejecutar en cada paso se deciden aleatoriamente. Esto es eficaz en aquellas aplicaciones inmaduras que han sido poco testeadas y presentan muchos errores. Dotar de “inteligencia” a los mecanismos de selección de acciones constituiría un importante avance para conseguir una mayor implantación de las herramientas de testeo automatizado, lo que redundaría en un incremento de la calidad del software. Éste es precisamente el objetivo de este trabajo. Para conseguirlo, se ha utilizado un enfoque basado en búsqueda (o search-based) que convierte el testeo en un problema de optimización. Nuestro punto de partida es la herramienta TESTAR, desarrollada en el ámbito del proyecto de investigación europeo FITTEST. Se han utilizado y evaluado dos métodos: Q-learning y programación genética. Otro resultado importante son la definición de las métricas apropiadas para optimizar; en este trabajo se han introducido cuatro nuevas métricas. La combinación de los algoritmos search-based con estas métricas ha permitido obtener resultados muy prometedores, que redundarán en la mejora de TESTAR.[EN] Graphic User Interfaces (GUI) are a main entry point to test an application. Different automated tools to test at the GUI level exist. Those that automate the design of test cases usually use random algorithms to choose the action that should be executed next in the test sequence. This technique is quite useful in applications that are immature, have been poorly tested or present many errors. To give more “intelligence” to this action selection mechanism, in this work we suppose a great development in the implantation of the automated testing tools. This improvement will result in better testing. To achieve this, we use search-based techniques to transform the problem into an optimization one. Our starting point is the tool called TESTAR, a tool developed during an EU research Project called FITTEST. Two different methods have been implemented and evaluated: Q-learning and genetic programming. Another results of our work is the definition of metrics that guide the optimization properly. Four new and different metrics have been introduced. The combination between metrics and search-based algorithms has been assessed and promising results have been obtained that will increase TESTAR capabilities.Almenar Pedrós, F. (2016). Testing Basado en la Busqueda en TESTAR. http://hdl.handle.net/10251/71699.TFG

Técnicas de paralelización de código para robots basados en emociones

[ES] Las arquitecturas de control basadas en emociones se están convirtiendo en una de las soluciones más prometedoras a la hora de implementar los sistemas de los robots. Los encargados de controlar estos sistemas son los procesos emocionales, que sirven de guía para el robot a la hora tomar una decisión sobre qué comportamientos se han de activar para completar sus objetivos. El número de estos procesos emocionales se incrementa en gran medida conforme lo hace la complejidad del problema, haciendo que la capacidad de cálculo de un procesador de un solo núcleo no sea suficiente para este trabajo. Por suerte, estos sistemas son altamente paralelizables y por lo tanto se puede incrementar la capacidad de cálculo del equipo en gran medida empleando técnicas de paralelización. En este TFG vamos a emplear distintas técnicas de paralelización con el objetivo de acelerar el cálculo de las emociones que determinarán el comportamiento de los robots. Para cumplir este objetivo utilizaremos y compararemos Unidades de Procesamiento Gráfico (GPU) con procesadores de múltiples núcleos y con el uso de instrucciones SIMD (Single Instruction Multiple Data).[EN] Control architectures based on emotions are becoming promising solutions for the implementation of future robotic systems. The basic controllers of this architecture are the emotional processes that decide which behaviors the robot must activate to fulfill the objectives. The number of emotional processes increases (hundreds of millions/s) with the complexity level of the application, limiting the processing capacity of the main processor to solve the complex problems. Fortunately, the potential parallelism of emotional processes permits their execution in parallel, hence enabling the power computing to tackle the complex dynamic problems. In this paper, Graphic Processing Unit (GPU), multicore processors and single instruction multiple data (SIMD) instructions are used to provide parallelism for the emotional processes.Almenar Pedros, F. (2014). Técnicas de paralelización de código para robots basados en emociones. http://hdl.handle.net/10251/48385.TFG

Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the TESTAR tool

[EN] Traversal-based automated software testing involves testing an application via its graphical user interface (GUI) and thereby taking the user's point of view and executing actions in a human-like manner. These actions are decided on the fly, as the software under test (SUT) is being run, as opposed to being set up in the form of a sequence prior to the testing, a sequence that is then used to exercise the SUT. In practice, random choice is commonly used to decide which action to execute at each state (a procedure commonly referred to as monkey testing), but a number of alternative mechanisms have also been proposed in the literature. Here we propose using genetic programming (GP) to evolve such an action selection strategy, defined as a list of IF-THEN rules. Genetic programming has proved to be suited for evolving all sorts of programs, and rules in particular, provided adequate primitives (functions and terminals) are defined. These primitives must aim to extract the most relevant information from the SUT and the dynamics of the testing process. We introduce a number of such primitives suited to the problem at hand and evaluate their usefulness based on various metrics. We carry out experiments and compare the results with those obtained by random selection and also by Q-learning, a reinforcement learning technique. Three applications are used as Software Under Test (SUT) in the experiments. The analysis shows the potential of GP to evolve action selection strategies.Esparcia Alcázar, AI.; Almenar-Pedrós, F.; Vos, TE.; Rueda Molina, U. (2018). Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the TESTAR tool. Memetic Computing. 10(3):257-265. https://doi.org/10.1007/s12293-018-0263-8S257265103Aho P, Menz N, Rty T (2013) Dynamic reverse engineering of GUI models for testing. 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