Made available in DSpace on 2014-12-17T15:48:08Z (GMT). No. of bitstreams: 1 EdmilsonBCN_DISSERT.pdf: 2688212 bytes, checksum: bae476692f237de556a79c9741333002 (MD5) Previous issue date: 2013-08-05 / The software systems development with domain-specific languages has become increasingly common. Domain-specific languages (DSLs) provide increased of the domain expressiveness, raising the abstraction level by facilitating the generation of models or low-level source code, thus increasing the productivity of systems development. Consequently, methods for the development of software product lines and software system families have also proposed the adoption of domain-specific languages. Recent studies have investigated the limitations of feature model expressiveness and proposing the use of DSLs as a complement or substitute for feature model. However, in complex projects, a single DSL is often insufficient to represent the different views and perspectives of development, being necessary to work with multiple DSLs. In order to address new challenges in this context, such as the management of consistency between DSLs, and the need to methods and tools that support the development with multiple DSLs, over the past years, several approaches have been proposed for the development of generative approaches. However, none of them considers matters relating to the composition of DSLs. Thus, with the aim to address this problem, the main objectives of this dissertation are: (i) to investigate the adoption of the integrated use of feature models and DSLs during the domain and application engineering of the development of generative approaches; (ii) to propose a method for the development of generative approaches with composition DSLs; and (iii) to investigate and evaluate the usage of modern technology based on models driven engineering to implement strategies of integration between feature models and composition of DSLs / A utiliza??o de linguagens espec?ficas de dom?nios para o desenvolvimento de sistemas de software tem se tornado cada vez mais comum. Elas propiciam um aumento da expressividade do dom?nio, elevando o seu n?vel de abstra??o atrav?s de facilidades para gera??o de modelos ou c?digos de baixo-n?vel, que aumentam assim a produtividade do desenvolvimento de sistemas. Como consequ?ncia, m?todos para o desenvolvimento de linhas de produtos de software e fam?lias de sistemas tamb?m t?m proposto a utiliza??o de linguagens espec?ficas de dom?nio (domain-specific languages DSLs). Estudos recentes t?m investigado os limites de expressividade do modelo de features, e propondo o uso de DSLs em sua substitui??o ou complemento. Contudo, em projetos complexos, uma ?nica DSL muitas vezes ? insuficiente para representar as diferentes vis?es e perspectivas do desenvolvimento, sendo necess?rio trabalhar com m?ltiplas DSLs. Com isso surgem novos desafios, tais como a ger?ncia de consist?ncia entre as DSLs, e a necessidade de m?todos e ferramentas que ofere?am suporte ao desenvolvimento com m?ltiplas DSLs. Ao longo dos ?ltimos anos, diversas abordagens t?m sido propostas para o desenvolvimento de abordagens generativas, entretanto, nenhuma delas considera quest?es relacionadas ? composi??o de DSLs. Assim, visando abordar tal problem?tica, os principais objetivos desta disserta??o s?o: (i) investigar a ado??o do uso integrado de modelos de features e DSLs tanto na engenharia de dom?nio quanto de aplica??o de desenvolvimento de abordagens generativas; (ii) propor um m?todo para o desenvolvimento de abordagens generativas com composi??o de DSLs; e (iii) investigar e avaliar o uso de tecnologias atuais de engenharia dirigida por modelos na implementa??o de estrat?gias de integra??o entre modelos de features e composi??o de DSLs

The dissertation covers the process of automatic software testing. It describes fundamental principles of tests creation and presents common tools for concurrent integration. The main objective of the dissertation is improvement of the automatic testing process in the Department Corporate Technology Development Center of the Company Siemens Ltd., which was realized through the plug-in module for the TeamCity tool that makes reporting of test results more transparent. At the end of the dissertation, there are designed extensions of the plug-in module that could have a further benefit for automatic testing process by means of the TeamCity tool. The dissertation also includes the manual for easy installation of the implemented solution.

<p>    </p> <p>Irregular control-flow structures like deeply nested conditional branches are common in real-world software applications. Improving the performance and efficiency of such programs is often challenging because it is difficult to analyze and optimize programs with irregular control flow. We observe that real-world programs contain similar or identical computations within different code paths of the conditional branches. Compilers can merge similar code to improve performance or code size. However, existing compiler optimizations like code hoisting/sinking, and tail merging do not fully exploit this opportunity. We propose a new technique called Control-Flow Melding (CFM) that can merge similar code sequences at the control-flow region level. We evaluate CFM in two applications. First, we show that CFM reduces the control divergence in GPU programs and improves the performance. Second, we apply CFM to CPU programs and show its effectiveness in reducing code size without sacrificing performance. In the next part of this dissertation, we investigate how CFM can be extended to improve dynamic test generation techniques like Dynamic Symbolic Execution (DSE). DSE suffers from path explosion problem when many conditional branches are present in the program. We propose a non-semantics-preserving branch elimination transformation called CFM-SE that reduces the number of symbolic branches in a program. We also provide a framework for detecting and reasoning about false positive bugs that might be added to the program by non-semantics-preserving transformations like CFM-SE. Furthermore, we evaluate CFM-SE on real-world applications and show its effectiveness in improving DSE performance and code coverage. </p>

Cette thèse a pour but d’améliorer l’automatisation dans l’ingénierie dirigée par les modèles (MDE pour Model Driven Engineering). MDE est un paradigme qui promet de réduire la complexité du logiciel par l’utilisation intensive de modèles et des transformations automatiques entre modèles (TM). D’une façon simplifiée, dans la vision du MDE, les spécialistes utilisent plusieurs modèles pour représenter un logiciel, et ils produisent le code source en transformant automatiquement ces modèles. Conséquemment, l’automatisation est un facteur clé et un principe fondateur de MDE. En plus des TM, d’autres activités ont besoin d’automatisation, e.g. la définition des langages de modélisation et la migration de logiciels. Dans ce contexte, la contribution principale de cette thèse est de proposer une approche générale pour améliorer l’automatisation du MDE. Notre approche est basée sur la recherche méta-heuristique guidée par les exemples. Nous appliquons cette approche sur deux problèmes importants de MDE, (1) la transformation des modèles et (2) la définition précise de langages de modélisation. Pour le premier problème, nous distinguons entre la transformation dans le contexte de la migration et les transformations générales entre modèles. Dans le cas de la migration, nous proposons une méthode de regroupement logiciel (Software Clustering) basée sur une méta-heuristique guidée par des exemples de regroupement. De la même façon, pour les transformations générales, nous apprenons des transformations entre modèles en utilisant un algorithme de programmation génétique qui s’inspire des exemples des transformations passées. Pour la définition précise de langages de modélisation, nous proposons une méthode basée sur une recherche méta-heuristique, qui dérive des règles de bonne formation pour les méta-modèles, avec l’objectif de bien discriminer entre modèles valides et invalides. Les études empiriques que nous avons menées, montrent que les approches proposées obtiennent des bons résultats tant quantitatifs que qualitatifs. Ceux-ci nous permettent de conclure que l’amélioration de l’automatisation du MDE en utilisant des méthodes de recherche méta-heuristique et des exemples peut contribuer à l’adoption plus large de MDE dans l’industrie à là venir. / This thesis aims to improve automation in Model Driven Engineering (MDE). MDE is a paradigm that promises to reduce software complexity by the mean of the intensive use of models and automatic model transformation (MT). Roughly speaking, in MDE vision, stakeholders use several models to represent the software, and produce source code by automatically transforming these models. Consequently, automation is a key factor and founding principle of MDE. In addition to MT, other MDE activities require automation, e.g. modeling language definition and software migration. In this context, the main contribution of this thesis is proposing a general approach for improving automation in MDE. Our approach is based on meta-heuristic search guided by examples. We apply our approach to two important MDE problems, (1) model transformation and (2) precise modeling languages. For transformations, we distinguish between transformations in the context of migration and general model transformations. In the case of migration, we propose a software clustering method based on a search algorithm guided by cluster examples. Similarly, for general transformations, we learn model transformations by a genetic programming algorithm taking inspiration from examples of past transformations. For the problem of precise metamodeling, we propose a meta-heuristic search method to derive well-formedness rules for metamodels with the objective of discriminating examples of valid and invalid models. Our empirical evaluation shows that the proposed approaches exhibit good results. These allow us to conclude that improving automation in MDE using meta-heuristic search and examples can contribute to a wider adoption of MDE in industry in the coming years.

Cette thèse a pour but d’améliorer l’automatisation dans l’ingénierie dirigée par les modèles (MDE pour Model Driven Engineering). MDE est un paradigme qui promet de réduire la complexité du logiciel par l’utilisation intensive de modèles et des transformations automatiques entre modèles (TM). D’une façon simplifiée, dans la vision du MDE, les spécialistes utilisent plusieurs modèles pour représenter un logiciel, et ils produisent le code source en transformant automatiquement ces modèles. Conséquemment, l’automatisation est un facteur clé et un principe fondateur de MDE. En plus des TM, d’autres activités ont besoin d’automatisation, e.g. la définition des langages de modélisation et la migration de logiciels. Dans ce contexte, la contribution principale de cette thèse est de proposer une approche générale pour améliorer l’automatisation du MDE. Notre approche est basée sur la recherche méta-heuristique guidée par les exemples. Nous appliquons cette approche sur deux problèmes importants de MDE, (1) la transformation des modèles et (2) la définition précise de langages de modélisation. Pour le premier problème, nous distinguons entre la transformation dans le contexte de la migration et les transformations générales entre modèles. Dans le cas de la migration, nous proposons une méthode de regroupement logiciel (Software Clustering) basée sur une méta-heuristique guidée par des exemples de regroupement. De la même façon, pour les transformations générales, nous apprenons des transformations entre modèles en utilisant un algorithme de programmation génétique qui s’inspire des exemples des transformations passées. Pour la définition précise de langages de modélisation, nous proposons une méthode basée sur une recherche méta-heuristique, qui dérive des règles de bonne formation pour les méta-modèles, avec l’objectif de bien discriminer entre modèles valides et invalides. Les études empiriques que nous avons menées, montrent que les approches proposées obtiennent des bons résultats tant quantitatifs que qualitatifs. Ceux-ci nous permettent de conclure que l’amélioration de l’automatisation du MDE en utilisant des méthodes de recherche méta-heuristique et des exemples peut contribuer à l’adoption plus large de MDE dans l’industrie à là venir. / This thesis aims to improve automation in Model Driven Engineering (MDE). MDE is a paradigm that promises to reduce software complexity by the mean of the intensive use of models and automatic model transformation (MT). Roughly speaking, in MDE vision, stakeholders use several models to represent the software, and produce source code by automatically transforming these models. Consequently, automation is a key factor and founding principle of MDE. In addition to MT, other MDE activities require automation, e.g. modeling language definition and software migration. In this context, the main contribution of this thesis is proposing a general approach for improving automation in MDE. Our approach is based on meta-heuristic search guided by examples. We apply our approach to two important MDE problems, (1) model transformation and (2) precise modeling languages. For transformations, we distinguish between transformations in the context of migration and general model transformations. In the case of migration, we propose a software clustering method based on a search algorithm guided by cluster examples. Similarly, for general transformations, we learn model transformations by a genetic programming algorithm taking inspiration from examples of past transformations. For the problem of precise metamodeling, we propose a meta-heuristic search method to derive well-formedness rules for metamodels with the objective of discriminating examples of valid and invalid models. Our empirical evaluation shows that the proposed approaches exhibit good results. These allow us to conclude that improving automation in MDE using meta-heuristic search and examples can contribute to a wider adoption of MDE in industry in the coming years.

Die Dissertationsschrift diskutiert die Machbarkeit einer modularen, portierbaren Middleware für die automatisierte Ausführung und Dokumentation von Software-Tests mit einer durchgehenden Nachverfolgbarkeit von der Anforderungsspezifikation bis hin zur Dokumentation der Testergebnisse. Es werden die Eigenschaften und Probleme bestehender Testautomatisierungslösungen analysiert und dargelegt. Unter Berücksichtigung dieser Probleme werden neuartige Lösungsansätze entwickelt. Die Neuheiten dieser Arbeit sind der modulare Aufbau der Middleware mit einer unproblematischen Portierbarkeit auf neue Testsysteme in Verbindung mit dem neu erarbeiteten Speicherformat für die Testergebnisse. Es wird die Möglichkeit aufgezeigt, Testfälle sowohl mit graphischer als auch textueller Eingabe zu bearbeiten. Neben den typischen Einsatzbereichen, wie zum Beispiel Hardware In The Loop-Tests (HIL), werden auch weitere Felder, vom Modul-Test bis zum Bandende-Test, abgedeckt. Das Speicherformat der Testergebnisse ermöglicht die Ablage aller wichtigen Informationen zu den Tests, ist flexibel erweiterbar und erlaubt die Generierung von Testreports in unterschiedlichen Zielformaten. Ein weiterer zentraler Punkt ist der automatisierte Austausch von Informationen und Testergebnissen mit verschiedenen Requirementsmanagement-Systemen sowie eine nahtlose Integration in vorhandene Versionsmanagement-Systeme. Basierend auf den theoretischen Ausarbeitungen wurde eine modulare, portierbare Middleware in Form des modularen aufgebauten Testautomatisierungs-Frameworks (modTF) umgesetzt. Anhand der dabei gesammelten Erfahrungen und der Ergebnisse der praktischen Erprobung werden die Vorteile des Frameworks gezeigt. / These PhD discusses the feasibility of a modular, portable middleware for automated execution and documentation of software tests with a continuous traceability from the requirements to the test results. The properties and the problems of existing test automation solutions are analyzed and presented. Based on the problems novel solutions are developed. The novelties of this PhD are the modular structure of the middleware with the easy portability to new test systems in cooperation with the novel storage format for the test results. The possibility for a graphical and textual description of the test cases is shown. Beside the typical applications like Hardware In The Loop tests (HIL) also applications from the module test to the line end test are include. The storage format for the results allows the storage of all needed information according to the test cases in one file. The format is flexible and extendable. The generation of test reports in different target formats is possible. Another imported point is the automated exchange of information and test results with different requirements management systems and the seamless integration in existing version management systems. By means of the collected experiences and the results of the practical proving the advantages of the framework are shown.

<p dir="ltr">Workflow management systems (WMSs) are commonly used to organize/automate sequences of tasks as workflows to accelerate scientific discoveries. During complex workflow modeling, a local interactive workflow environment is desirable, as users usually rely on their rich, local environments for fast prototyping and refinements before they consider using more powerful computing resources.</p><p dir="ltr">This dissertation delves into the innovative development of the CyberWater framework based on Workflow Management Systems (WMSs). Against the backdrop of data-intensive and complex models, CyberWater exemplifies the transition of intricate data into insightful and actionable knowledge and introduces the nuanced architecture of CyberWater, particularly focusing on its adaptation and enhancement from the VisTrails system. It highlights the significance of control and data flow mechanisms and the introduction of new data formats for effective data processing within the CyberWater framework.</p><p dir="ltr">This study presents an in-depth analysis of the design and implementation of Generic Model Agent Toolkits. The discussion centers on template-based component mechanisms and the integration with popular platforms, while emphasizing the toolkit’s ability to facilitate on-demand access to High-Performance Computing resources for large-scale data handling. Besides, the development of an asynchronously controlled workflow within CyberWater is also explored. This innovative approach enhances computational performance by optimizing pipeline-level parallelism and allows for on-demand submissions of HPC jobs, significantly improving the efficiency of data processing.</p><p dir="ltr">A comprehensive methodology for model-driven development and Python code integration within the CyberWater framework and innovative applications of GPT models for automated data retrieval are introduced in this research as well. It examines the implementation of Git Actions for system automation in data retrieval processes and discusses the transformation of raw data into a compatible format, enhancing the adaptability and reliability of the data retrieval component in the adaptive generic model agent toolkit component.</p><p dir="ltr">For the development and maintenance of software within the CyberWater framework, the use of tools like GitHub for version control and outlining automated processes has been applied for software updates and error reporting. Except that, the user data collection also emphasizes the role of the CyberWater Server in these processes.</p><p dir="ltr">In conclusion, this dissertation presents our comprehensive work on the CyberWater framework's advancements, setting new standards in scientific workflow management and demonstrating how technological innovation can significantly elevate the process of scientific discovery.</p>