Evolutionary computation for software testing

A variety of products undergo a transformation from a pure mechanical design to more and more software and electronic components. A polarized example are watches. Several decades ago they have been purely mechanical. Modern smart watches are almost completely electronic devices which heavily rely on software. Further, a smart watch offers a lot more features than just the information about the current time. This change had a crucial impact on how software is being developed. A first attempt to control the rising complexity was to move to agile development practices such as extreme programming or scrum. This rise in complexity is not only affecting the development process but also quality assurance and software testing. If a product contains more and more features then this leads to a higher number of tests necessary to ensure quality standards. Furthermore agile development practices work in an iterative manner which leads to repetitive testing that puts more effort on the testing team. We aimed within the thesis to ease the pain of testing. Thereby we examined a series of subproblems that arise. A key complexity is the number of test cases. We intended to reduce the number of test cases before they are executed manually or implemented as automated tests. Thereby we examined the test specification and based on the requirements coverage of the individual tests, we were able to identify redundant tests. We relied on a novel metaheuristic called GCAIS which we improved upon iteratively. Another task is to control the remaining complexity. Testing is often time crucial and an appropriate subset of the available tests must be chosen in order to get a quick insight into the status of the device under test. We examined this challenge in two different testing scenarios. The first scenario is located in semi-automated testing where engineers execute a set of automated tests locally and closely observe the behaviour of the system under test. We extended GCAIS to compute test suites that satisfy different criteria if provided with sufficient search time. The second use case is located in fully automated testing in a continuous integration (CI) setting. CI focuses on frequent software build cycles which also include testing. These builds contain a testing stage which greatly emphasizes speed. Thus there we also have to compute crucial tests. However, due to the nature of the process we have to continuously recompute a test suite for each build as the software and maybe even the test cases at hand have changed. Hence it is hard to compute the test suite ahead of time and these tests have to be determined as part of the CI execution. Thus we switched to a computational lightweight learning classifier system (LCS) to prioritize and select test cases. We integrated a series of innovations we made into an LCS known as XCSF such as continuous priorities, experience replay and transfer learning. This enabled us to outperform a state of the art artificial neural network which is used by companies such as Netflix. We further investigated how LCS can be made faster using parallelism. We developed generic approaches which may run on any multicore computing device. This is of interest for our CI use case as the build server's architecture is unknown. However, the methods are also independent of the concrete LCS and are not linked to our testing problem. We identified that many of the challenges that need to be faced in the CI use case have been tackled by Organic Computing (OC), for example the need to adapt to an ever changing environment. Hence we relied on OC design principles to create a system architecture which wraps the LCS developed and integrates it into existing CI processes. The final system is robust and highly autonomous. A side-effect of the high degree of autonomy is a high level of automatization which fits CI well. We also gave insight on the usability and delivery of the full system to our industrial partner. Test engineers can easily integrate it with a few lines of code and need no knowledge about LCS and OC in order to use it. Another implication of the developed system is that OC's ideas and design principles can also be employed outside the field of embedded systems. This shows that OC has a greater level of generality. The process of testing and correcting found errors is still only partially automated. We make a first step into automating the entire process and thereby take an analogy to the concept of self-healing of OC. As a first proof of concept of this school of thought we take a look at touch interfaces. There we can automatically manipulate the software to fulfill the specified behaviour. Thus only a minimalistic amount of manual work is required

Comparison of Adaptive Behaviors of an Animat in Different Markovian 2-D Environments Using XCS Classifier Systems

RÉSUMÉ Le mot "Animat" fut introduit par Stewart W. Wilson en 1985 et a rapidement gagné en popularité dans la lignée des conférences SAB (Simulation of Adaptive Behavior: From Animals to Animats) qui se sont tenues entre 1991 à 2010. Comme la signification du terme "animat" a passablement évoluée au cours de ces années, il est important de préciser que nous avons choisi d'étudier l'animat tel que proposée originellement par Wilson. La recherche sur les animats est un sous-domaine du calcul évolutif, de l'apprentissage machine, du comportement adaptatif et de la vie artificielle. Le but ultime des recherches sur les animats est de construire des animaux artificiels avec des capacités sensorimotrices limitées, mais capables d'adopter un comportement adaptatif pour survivre dans un environnement imprévisible. Différents scénarios d'interaction entre un animat et un environnement donné ont été étudiés et rapportés dans la littérature. Un de ces scénario est de considérer un problème d'animat comme un problème d'apprentissage par renforcement (tel que les processus de décision markovien) et de le résoudre par l'apprentissage de systèmes de classeurs (LCS, Learning Classification Systems) possédant une certaine capacité de généralisation. L'apprentissage d'un système de classification LCS est équivalent à un système qui peut apprendre des chaînes simples de règles en interagissant avec l'environnement et en reçevant diverses récompenses. Le XCS (eXtended Classification System) introduit par Wilson en 1995 est le LCS le plus populaire actuellement. Il utilise le Q-Learning pour résoudre les problèmes d'affectation de crédit (récompense), et il sépare les variables d'adaptation de l'algorithme génétique de celles reliées au mécanisme d'attribution des récompenses. Dans notre recherche, nous avons étudié les performances de XCS, et plusieurs de ses variantes, pour gérer un animat explorant différents types d'environnements 2D à la recherche de nourriture. Les environnements 2D traditionnellement nommés WOODS1, WOODS2 et MAZE5 ont été étudiés, de même que des environnements S2DM (Square 2D Maze) que nous avons conçus pour notre étude. Les variantes de XCS sont XCSS (avec l'opérateur "Specify" qui permet de diminuer la portée de certains classificateurs), et XCSG (avec la descente du gradient en fonction des valeurs de prédiction).---------- Abstract The word “Animat” was introduced by Stewart W. Wilson in 1985 and became popular since the SAB line conferences “Simulation of Adaptive Behavior: from Animals to Animats” that were held between 1991 and 2010. Since the use of this word in the scientific literature has fairly evolved over the years, it is important to specify in this thesis that we have chosen to adopt the definition that was originally proposed by Wilson. The research on animat is a subfield of evolutionary computation, machine learning, adaptive behavior and artificial life. The ultimate goal of animat research is to build artificial animals with limited sensory-motor capabilities but able to behave in an adaptive way to survive in an unknown environment. Different scenarios of interaction between a given animat and a given environment have been studied and reported in the literature. One of the scenarios is to consider animat problems as a reinforcement learning problem (such as a Markov decision processes) and solve it by Learning Classifier Systems (LCS) with certain generalization ability. A Learning classifier system is equivalent to a learning system that can learn simple strings of rules by interacting with the environment and receiving diverse payoffs (rewards). The XCS (eXtented Classification System) [1], introduced by Wilson in 1995, is the most popular Learning Classifier System at the moment. It uses Q-learning to deal with the problem of credit assignment and it separates the fitness variable for genetic algorithm from those linked to credit assignment mechanisms. In our research, we have studied XCS performances and many of its variants, to manage an animat exploring different types of 2D environments in search of food. 2D environments traditionally named WOODS1, WOODS 2 and MAZE5 have been studied, as well as several designed S2DM (SQUARE 2D MAZE) environments which we have conceived for our study. The variants of XCS are XCSS (with the Specify operator which allows removing detrimental rules), and XCSG (using gradient descent according to the prediction value)

MILCS: A mutual information learning classifier system

This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. MILCS's design draws on an analogy to the structural learning approach of cascade correlation networks. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets, and introduce a new technique for visualizing explanatory power. Final comments include future directions for this research, including investigations in neural networks and other systems. Copyright 2007 ACM

Three-cornered coevolution learning classifier systems for classification

This thesis introduces a Three-Cornered Coevolution System that is capable of addressing classification tasks through coevolution (coadaptive evolution) where three different agents (i.e. a generation agent and two classification agents) learn and adapt to the changes of the problems without human involvement. In existing pattern classification systems, humans usually play a major role in creating and controlling the problem domain. In particular, humans set up and tune the problem’s difficulty. A motivation of the work for this thesis is to design and develop an automatic pattern generation and classification system that can generate various sets of exemplars to be learned from and perform the classification tasks autonomously. The system should be able to automatically adjust the problem’s difficulty based on the learners’ ability to learn (e.g. determining features in the problem that affect the learners’ performance in order to generate various problems for classification at different levels of difficulty). Further, the system should be capable of addressing the classification tasks through coevolution (coadaptive evolution), where the participating agents learn and adapt to the changes of the problems without human participation. Ultimately, Learning Classifier System (LCS) is chosen to be implemented in the participating agents. LCS has several potential characteristics, such as interpretability, generalisation capability and variations in representation, that are suitable for the system. The work can be broken down into three main phases. Phase 1 is to develop an automated evolvable problem generator to autonomously generate various problems for classification, Phase 2 is to develop the Two-Cornered Coevolution System for classification, and Phase 3 is to develop the Three-Cornered Coevolution System for classification. Phase 1 is necessary in order to create a set of problem domains for classification (i.e. image-based data or artificial data) that can be generated automatically, where the difficulty levels of the problem can be adjusted and tuned. Phase 2 is needed to investigate the generation agent’s ability to autonomously tune and adjust the problem’s difficulty based on the classification agent’s performance. Phase 2 is a standard coevolution system, where two different agents evolve to adapt to the changes of the problem. The classification agent evolves to learn various classification problems, while the generation agent evolves to tune and adjust the problem’s difficulty based on the learner’s ability to learn. Phase 3 is the final research goal. This phase develops a new coevolution system where three different agents evolve to adapt to the changes of the problem. Both of the classification agents evolve to learn various classification problems, while the generation agent evolves to tune and adjust the problem’s difficulty based on the classification agents’ ability to learn. The classification agents use different styles of learning techniques (i.e. supervised or reinforcement learning techniques) to learn the problems. Based on the classification agents’ ability (i.e. the difference in performance between the classification agents) the generation agent adjusts and creates various problems for classification at different levels of difficulty (i.e. various ‘hard’ problems). The Three-Cornered Coevolution System offers a great potential for autonomous learning and provides useful insight into coevolution learning over the standard studies of pattern recognition. The system is capable of autonomously generating various problems, learning and providing insight into each learning system’s ability by determining the problem domains where they perform relatively well. This is in contrast to humans having to determine the problem domains

学習戦略に基づく学習分類子システムの設計

On Learning Classifier Systems dubbed LCSs a leaning strategy which defines how LCSs cover a state-action space in a problem can be one of the most fundamental options in designing LCSs. There lacks an intensive study of the learning strategy to understand whether and how the learning strategy affects the performance of LCSs. This lack has resulted in the current design methodology of LCS which does not carefully consider the types of learning strategy. The thesis clarifies a need of a design methodology of LCS based on the learning strategy. That is, the thesis shows the learning strategy can be an option that determines the potential performance of LCSs and then claims that LCSs should be designed on the basis of the learning strategy in order to improve the performance of LCSs. First, the thesis empirically claims that the current design methodology of LCS, without the consideration of learning strategy, can be limited to design a proper LCS to solve a problem. This supports the need of design methodology based on the learning strategy. Next, the thesis presents an example of how LCS can be designed on the basis of the learning strategy. The thesis empirically show an adequate learning strategy improving the performance of LCS can be decided depending on a type of problem difficulties such as missing attributes. Then, the thesis draws an inclusive guideline that explains which learning strategy should be used to address which types of problem difficulties. Finally, the thesis further shows, on an application of LCS for a human daily activity recognition problem, the adequate learning strategy according to the guideline effectively improves the performance of the application. The thesis concludes that the learning strategy is the option of the LCS design which determines the potential performance of LCSs. Thus, before designing any type of LCSs including their applications, the learning strategy should be adequately selected at first, because their performance degrades when they employ an inadequate learning strategy to a problem they want to solve. In other words, LCSs should be designed on the basis of the adequate learning strategy.電気通信大学201

Towards adaptive and autonomous humanoid robots: from vision to actions

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions

Systems and control : 21th Benelux meeting, 2002, March 19-21, Veldhoven, The Netherlands

Book of abstract

Organic traffic control

Modern cities cannot be imagined without traffic lights controlling the road network. To handle the network\u27s changing demands efficiently, the signal plan specification needs to be shifted from the design time to the run-time of a signal system. The generic observer/controller architecture proposed for Organic Computing facilitates this shift. A two-levelled learning mechanism optimises signal plans on-line while a distributed coordination mechanism establishes green waves in the road network