Real-time Detection of Vehicles Using the Haar-like Features and Artificial Neuron Networks

AbstractIn this document, a vehicle detection system is presented. This system is based on two algorithms, a descriptor of the image type haar-like, and a classifier type artificial neuron networks. In order to ensure rapidity in the calculation extracts features by the descriptor the concept of the integral image is used for the representation of the image. The learning of the system is performed on a set of positive images (vehicles) and negative images (non-vehicle), and the test is done on another set of scenes (positive or negative). To address the performance of the proposed system by varying one element among the determining parameters which is the number of neurons in the hidden layer; the results obtained have shown that the proposed system is a fast and robust vehicle detector

NOTICE: A Framework for Non-functional Testing of Compilers

International audience—Generally, compiler users apply different optimizations to generate efficient code with respect to non-functional properties such as energy consumption, execution time, etc. However, due to the huge number of optimizations provided by modern compilers, finding the best optimization sequence for a specific objective and a given program is more and more challenging. This paper proposes NOTICE, a component-based framework for non-functional testing of compilers through the monitoring of generated code in a controlled sand-boxing environment. We evaluate the effectiveness of our approach by verifying the optimizations performed by the GCC compiler. Our experimental results show that our approach is able to auto-tune compilers according to user requirements and construct optimizations that yield to better performance results than standard optimization levels. We also demonstrate that NOTICE can be used to automatically construct optimization levels that represent optimal trade-offs between multiple non-functional properties such as execution time and resource usage requirements

A Novelty Search-based Test Data Generator for Object-oriented Programs

International audienceIn search-based structural testing, meta-heuristic search techniques have been frequently used to automate test data generation. In this paper, we introduce the use of novelty search algorithm to the test data generation problem based on statement-covered criterion. In this approach, we seek to explore the search space by considering diversity as the unique objective function to be optimized. In fact, instead of having a fitness-based selection, we select test cases based on a novelty score showing how different they are compared to all other solutions evaluated so far

Automatic Non-functional Testing of Code Generators Families

International audienceThe intensive use of generative programming techniques provides an elegant engineering solution to deal with the heterogeneity of platforms and technological stacks. The use of domain-specific languages for example, leads to the creation of numerous code generators that automatically translate highlevel system specifications into multi-target executable code. Producing correct and efficient code generator is complex and error-prone. Although software designers provide generally high-level test suites to verify the functional outcome of generated code, it remains challenging and tedious to verify the behavior of produced code in terms of non-functional properties. This paper describes a practical approach based on a runtime monitoring infrastructure to automatically check the potential inefficient code generators. This infrastructure, based on system containers as execution platforms, allows code-generator developers to evaluate the generated code performance. We evaluate our approach by analyzing the performance of Haxe, a popular high-level programming language that involves a set of cross-platform code generators. Experimental results show that our approach is able to detect some performance inconsistencies that reveal real issues in Haxe code generators

NOTICE: A Framework for Non-functional Testing of Compilers

International audience—Generally, compiler users apply different optimizations to generate efficient code with respect to non-functional properties such as energy consumption, execution time, etc. However, due to the huge number of optimizations provided by modern compilers, finding the best optimization sequence for a specific objective and a given program is more and more challenging. This paper proposes NOTICE, a component-based framework for non-functional testing of compilers through the monitoring of generated code in a controlled sand-boxing environment. We evaluate the effectiveness of our approach by verifying the optimizations performed by the GCC compiler. Our experimental results show that our approach is able to auto-tune compilers according to user requirements and construct optimizations that yield to better performance results than standard optimization levels. We also demonstrate that NOTICE can be used to automatically construct optimization levels that represent optimal trade-offs between multiple non-functional properties such as execution time and resource usage requirements

Une approche pour le test non-fonctionnel et la configuration automatique des générateurs

Generative software development has paved the way for the creation of multiple generators (code generators and compilers) that serve as a basis for automatically producing code to a broad range of software and hardware platforms. With full automatic code generation, users are able to rapidly synthesize software artifacts for various software platforms. In addition, they can easily customize the generated code for the target hardware platform since modern generators (i.e., C compilers) become highly configurable, offering numerous configuration options that the user can apply. Consequently, the quality of generated software becomes highly correlated to the configuration settings as well as to the generator itself. In this context, it is crucial to verify the correct behavior of generators. Numerous approaches have been proposed to verify the functional outcome of generated code but few of them evaluate the non-functional properties of automatically generated code, namely the performance and resource usage properties. This thesis addresses three problems : (1) Non-functional testing of generators: We benefit from the existence of multiple code generators with comparable functionality (i.e., code generator families) to automatically test the generated code. We leverage the metamorphic testing approach to detect non-functional inconsistencies in code generator families by defining metamorphic relations as test oracles. We define the metamorphic relation as a comparison between the variations of performance and resource usage of code, generated from the same code generator family. We evaluate our approach by analyzing the performance of HAXE, a popular code generator family. Experimental results show that our approach is able to automatically detect several inconsistencies that reveal real issues in this family of code generators. (2) Generators auto-tuning: We exploit the recent advances in search-based software engineering in order to provide an effective approach to tune generators (i.e., through optimizations) according to user's non-functional requirements (i.e., performance and resource usage). We also demonstrate that our approach can be used to automatically construct optimization levels that represent optimal trade-offs between multiple non-functional properties such as execution time and resource usage requirements. We evaluate our approach by verifying the optimizations performed by the GCC compiler. Our experimental results show that our approach is able to auto-tune compilers and construct optimizations that yield to better performance results than standard optimization levels. (3) Handling the diversity of software and hardware platforms in software testing: Running tests and evaluating the resource usage in heterogeneous environments is tedious. To handle this problem, we benefit from the recent advances in lightweight system virtualization, in particular container-based virtualization, in order to offer effective support for automatically deploying, executing, and monitoring code in heterogeneous environment, and collect non-functional metrics (e.g., memory and CPU consumptions). This testing infrastructure serves as a basis for evaluating the experiments conducted in the two first contributions.Les techniques émergentes de l’ingénierie dirigée par les modèles et de la programmation générative ont permis la création de plusieurs générateurs (générateurs de code et compilateurs). Ceux-ci sont souvent utilisés afin de faciliter le développement logiciel et automatiser le processus de génération de code à partir des spécifications abstraites. De plus, les générateurs modernes comme les compilateurs C, sont devenus hautement configurables, offrant de nombreuses options de configuration à l'utilisateur de manière à personnaliser facilement le code généré pour la plateforme matérielle cible. Par conséquent, la qualité logicielle est devenue fortement corrélée aux paramètres de configuration ainsi qu'au générateur lui-même. Dans ce contexte, il est devenu indispensable de vérifier le bon comportement des générateurs. Cette thèse établit trois contributions principales : Contribution I: détection automatique des inconsistances dans les familles de générateurs de code : Dans cette contribution, nous abordons le problème de l'oracle dans le domaine du test non-fonctionnel des générateurs de code. La disponibilité de multiples générateurs de code avec des fonctionnalités comparables (c.-à-d. familles de générateurs de code) nous permet d'appliquer l'idée du test métamorphique en définissant des oracles de test de haut-niveau (c.-à-d. relation métamorphique) pour détecter des inconsistances. Une inconsistance est détectée lorsque le code généré présente un comportement inattendu par rapport à toutes les implémentations équivalentes de la même famille. Nous évaluons notre approche en analysant la performance de Haxe, un langage de programmation de haut niveau impliquant un ensemble de générateurs de code multi-plateformes. Les résultats expérimentaux montrent que notre approche est capable de détecter plusieurs inconsistances qui révèlent des problèmes réels dans cette famille de générateurs de code. Contribution II: une approche pour l'auto-configuration des compilateurs. Le grand nombre d'options de compilation des compilateurs nécessite une méthode efficace pour explorer l'espace d’optimisation. Ainsi, nous appliquons, dans cette contribution, une méta-heuristique appelée Novelty Search pour l'exploration de cet espace de recherche. Cette approche aide les utilisateurs à paramétrer automatiquement les compilateurs pour une architecture matérielle cible et pour une métrique non-fonctionnelle spécifique tel que la performance et l'utilisation des ressources. Nous évaluons l'efficacité de notre approche en vérifiant les optimisations fournies par le compilateur GCC. Nos résultats expérimentaux montrent que notre approche permet d'auto-configurer les compilateurs en fonction des besoins de l'utilisateur et de construire des optimisations qui surpassent les niveaux d'optimisation standard. Nous démontrons également que notre approche peut être utilisée pour construire automatiquement des niveaux d'optimisation qui représentent des compromis optimaux entre plusieurs propriétés non-fonctionnelles telles que le temps d'exécution et la consommation des ressources. Contribution III: Un environnement d'exécution léger pour le test et la surveillance de la consommation des ressources des logiciels. Enfin, nous proposons une infrastructure basée sur les micro-services pour assurer le déploiement et la surveillance de la consommation des ressources des différentes variantes du code généré. Cette contribution traite le problème de l'hétérogénéité des plateformes logicielles et matérielles. Nous décrivons une approche qui automatise le processus de génération, compilation, et exécution du code dans le but de faciliter le test et l'auto-configuration des générateurs. Cet environnement isolé repose sur des conteneurs système, comme plateformes d'exécution, pour une surveillance et analyse fine des propriétés liées à l'utilisation des ressources (CPU et mémoire)

A Novelty Search Approach for Automatic Test Data Generation

International audienceIn search-based structural testing, metaheuristic search techniques have been frequently used to automate the test data generation. In Genetic Algorithms (GAs) for example, test data are rewarded on the basis of an objective function that represents generally the number of statements or branches covered. However, owing to the wide diversity of possible test data values, it is hard to find the set of test data that can satisfy a specific coverage criterion. In this paper, we introduce the use of Novelty Search (NS) algorithm to the test data generation problem based on statement-covered criteria. We believe that such approach to test data generation is attractive because it allows the exploration of the huge space of test data within the input domain. In this approach, we seek to explore the search space without regard to any objectives. In fact, instead of having a fitness-based selection, we select test cases based on a novelty score showing how different they are compared to all other solutions evaluated so far

On the best volume fraction distributions for functionally graded cylinders, spheres and disks – A pseudospectral approach

International audienceThe paper is devoted to the optimization of axisymmetric structures made of functionally graded materials and subject to mechanical and thermal loads. The novelty of the results is that the volume fraction distribution is not limited to a power-law variation, as in most of the works available in the literature, but can be any (piecewise continuous) function. This approach leads to an intrinsic tailoring approach, in the sense it occurs without prefixing the spatial distribution of effective mechanical properties a priori, and therefore exploiting at best the inhomogeneity of functionally graded material. After recalling the governing equations and showing some recent results concerning candidate solutions for the optimal volume fraction distribution in some particular cases, several instances of the optimization problems aiming at minimizing occurring maximum stresses are formulated. We show that all these formulations can be treated within the same numerical approach based on the so-called pseudospectral methods. In the last part of the paper we describe how these methods have been effectively applied to the considered problems and we discuss the yielded solutions comparing them, where possible, with power-law solutions