Revisión sistemática de la integración de modelos de desarrollo de software dirigido por modelos y metodologías ágiles

Currently, in some instances of the software development industry are carried out by means of manual activities and/or robust methodologies which can be often heavy and inefficient. This situation brings several issues related to the difficulty to produce software in a timely manner, agile, at low cost and with a high quality level. A way to improve this situation is to incorporate in the software development process the formalism and abstraction needed to automate and optimize the most critical tasks defined from methodologies used in software companies and starting from an agile approach. This would add value to the business and would improve significantly the process of software. In this sense, in order to publicize the benefits of agile approaches and programming environments driven models, a systematic review of the literature has been conducted so as to the projects where these approaches have been integrated globally. Besides, it has been possible to identify some benefits, which have been reported by different studies.Actualmente, en algunas instancias, la industria de desarrollo de software se lleva a cabo por medio de actividades manuales y/o metodologías robustas que pueden llegar a ser en muchos casos pesadas e ineficientes. Esta situación trae consigo algunos problemas relacionados con la dificultad para producir software de manera oportuna, ágil, a bajo costo y con un alto nivel de calidad. Una manera de mejorar esta situación está en añadir al proceso de desarrollo de software el formalismo y la abstracción necesaria que permita automatizar y optimizar las tareas más críticas definidas, a partir de las metodologías utilizadas en las empresas de software, y desde una perspectiva ágil. Esto añadiría valor agregado a los negocios y mejoraría el proceso de software considerablemente. En este sentido, con el objetivo de conocer las bondades de los enfoques ágiles y los entornos de programación dirigidos por modelos, se llevó a cabo una revisión sistemática de la literatura en relación con los proyectos donde se integran estos enfoques a nivel mundial, así como la identificació

Towards using intelligent techniques to assist software specialists in their tasks

L’automatisation et l’intelligence constituent des préoccupations majeures dans le domaine de l’Informatique. Avec l’évolution accrue de l’Intelligence Artificielle, les chercheurs et l’industrie se sont orientés vers l’utilisation des modèles d’apprentissage automatique et d’apprentissage profond pour optimiser les tâches, automatiser les pipelines et construire des systèmes intelligents. Les grandes capacités de l’Intelligence Artificielle ont rendu possible d’imiter et même surpasser l’intelligence humaine dans certains cas aussi bien que d’automatiser les tâches manuelles tout en augmentant la précision, la qualité et l’efficacité. En fait, l’accomplissement de tâches informatiques nécessite des connaissances, une expertise et des compétences bien spécifiques au domaine. Grâce aux puissantes capacités de l’intelligence artificielle, nous pouvons déduire ces connaissances en utilisant des techniques d’apprentissage automatique et profond appliquées à des données historiques représentant des expériences antérieures. Ceci permettra, éventuellement, d’alléger le fardeau des spécialistes logiciel et de débrider toute la puissance de l’intelligence humaine. Par conséquent, libérer les spécialistes de la corvée et des tâches ordinaires leurs permettra, certainement, de consacrer plus du temps à des activités plus précieuses. En particulier, l’Ingénierie dirigée par les modèles est un sous-domaine de l’informatique qui vise à élever le niveau d’abstraction des langages, d’automatiser la production des applications et de se concentrer davantage sur les spécificités du domaine. Ceci permet de déplacer l’effort mis sur l’implémentation vers un niveau plus élevé axé sur la conception, la prise de décision. Ainsi, ceci permet d’augmenter la qualité, l’efficacité et productivité de la création des applications. La conception des métamodèles est une tâche primordiale dans l’ingénierie dirigée par les modèles. Par conséquent, il est important de maintenir une bonne qualité des métamodèles étant donné qu’ils constituent un artéfact primaire et fondamental. Les mauvais choix de conception, ainsi que les changements conceptuels répétitifs dus à l’évolution permanente des exigences, pourraient dégrader la qualité du métamodèle. En effet, l’accumulation de mauvais choix de conception et la dégradation de la qualité pourraient entraîner des résultats négatifs sur le long terme. Ainsi, la restructuration des métamodèles est une tâche importante qui vise à améliorer et à maintenir une bonne qualité des métamodèles en termes de maintenabilité, réutilisabilité et extensibilité, etc. De plus, la tâche de restructuration des métamodèles est délicate et compliquée, notamment, lorsqu’il s’agit de grands modèles. De là, automatiser ou encore assister les architectes dans cette tâche est très bénéfique et avantageux. Par conséquent, les architectes de métamodèles pourraient se concentrer sur des tâches plus précieuses qui nécessitent de la créativité, de l’intuition et de l’intelligence humaine. Dans ce mémoire, nous proposons une cartographie des tâches qui pourraient être automatisées ou bien améliorées moyennant des techniques d’intelligence artificielle. Ensuite, nous sélectionnons la tâche de métamodélisation et nous essayons d’automatiser le processus de refactoring des métamodèles. A cet égard, nous proposons deux approches différentes: une première approche qui consiste à utiliser un algorithme génétique pour optimiser des critères de qualité et recommander des solutions de refactoring, et une seconde approche qui consiste à définir une spécification d’un métamodèle en entrée, encoder les attributs de qualité et l’absence des design smells comme un ensemble de contraintes et les satisfaire en utilisant Alloy.Automation and intelligence constitute a major preoccupation in the field of software engineering. With the great evolution of Artificial Intelligence, researchers and industry were steered to the use of Machine Learning and Deep Learning models to optimize tasks, automate pipelines, and build intelligent systems. The big capabilities of Artificial Intelligence make it possible to imitate and even outperform human intelligence in some cases as well as to automate manual tasks while rising accuracy, quality, and efficiency. In fact, accomplishing software-related tasks requires specific knowledge and skills. Thanks to the powerful capabilities of Artificial Intelligence, we could infer that expertise from historical experience using machine learning techniques. This would alleviate the burden on software specialists and allow them to focus on valuable tasks. In particular, Model-Driven Engineering is an evolving field that aims to raise the abstraction level of languages and to focus more on domain specificities. This allows shifting the effort put on the implementation and low-level programming to a higher point of view focused on design, architecture, and decision making. Thereby, this will increase the efficiency and productivity of creating applications. For its part, the design of metamodels is a substantial task in Model-Driven Engineering. Accordingly, it is important to maintain a high-level quality of metamodels because they constitute a primary and fundamental artifact. However, the bad design choices as well as the repetitive design modifications, due to the evolution of requirements, could deteriorate the quality of the metamodel. The accumulation of bad design choices and quality degradation could imply negative outcomes in the long term. Thus, refactoring metamodels is a very important task. It aims to improve and maintain good quality characteristics of metamodels such as maintainability, reusability, extendibility, etc. Moreover, the refactoring task of metamodels is complex, especially, when dealing with large designs. Therefore, automating and assisting architects in this task is advantageous since they could focus on more valuable tasks that require human intuition. In this thesis, we propose a cartography of the potential tasks that we could either automate or improve using Artificial Intelligence techniques. Then, we select the metamodeling task and we tackle the problem of metamodel refactoring. We suggest two different approaches: A first approach that consists of using a genetic algorithm to optimize set quality attributes and recommend candidate metamodel refactoring solutions. A second approach based on mathematical logic that consists of defining the specification of an input metamodel, encoding the quality attributes and the absence of smells as a set of constraints and finally satisfying these constraints using Alloy

Evolution of security engineering artifacts: a state of the art survey

Security is an important quality aspect of modern open software systems. However, it is challenging to keep such systems secure because of evolution. Security evolution can only be managed adequately if it is considered for all artifacts throughout the software development lifecycle. This article provides state of the art on the evolution of security engineering artifacts. The article covers the state of the art on evolution of security requirements, security architectures, secure code, security tests, security models, and security risks as well as security monitoring. For each of these artifacts the authors give an overview of evolution and security aspects and discuss the state of the art on its security evolution in detail. Based on this comprehensive survey, they summarize key issues and discuss directions of future research

Context-aware Process Management for the Software Engineering Domain

Historically, software development projects are challenged with problems concerning budgets, deadlines and the quality of the produced software. Such problems have various causes like the high number of unplanned activities and the operational dynamics present in this domain. Most activities are knowledge-intensive and require collaboration of various actors. Additionally, the produced software is intangible and therefore difficult to measure. Thus, software producers are often insufficiently aware of the state of their source code, while suitable software quality measures are often applied too late in the project lifecycle, if at all. Software development processes are used by the majority of software companies to ensure the quality and reproducibility of their development endeavors. Typically, these processes are abstractly defined utilizing process models. However, they still need to be interpreted by individuals and be manually executed, resulting in governance and compliance issues. The environment is sufficiently dynamic that unforeseen situations can occur due to various events, leading to potential aberrations and process governance issues. Furthermore, as process models are implemented manually without automation support, they impose additional work for the executing humans. Their advantages often remain hidden as aligning the planned process with reality is cumbersome. In response to these problems, this thesis contributes the Context-aware Process Management (CPM) framework. The latter enables holistic and automated support for software engineering projects and their processes. In particular, it provides concepts for extending process management technology to support software engineering process models in their entirety. Furthermore, CPM contributes an approach to integrate the enactment of the process models better with the real-world process by introducing a set of contextual extensions. Various events occurring in the course of the projects can be utilized to improve process support and activities outside the realm of the process models can be covered. That way, the continuously growing divide between the plan and reality that often occurs in software engineering projects can be avoided. Finally, the CPM framework comprises facilities to better connect the software engineering process with other important aspects and areas of software engineering projects. This includes automated process-oriented support for software quality management or software engineering knowledge management. The CPM framework has been validated by a prototypical implementation, various sophisticated scenarios, and its practical application at two software companies

Full Stack Application Generation for Insurance Sales based on Product Models

The insurance market is segregated in various lines-of-business such as Life, Health, Property & Casualty, among others. This segregation allows product engineers to focus on the rules and details of a speci c insurance area. However, having di erent conceptual models leads to an additional complexity when a generic presentation layer application has to be continuously adapted to work with these distinct models. With the objective to streamline these continuous adaptations in an existent presentation layer, this work investigates and proposes the usage of code generators to allow a complete application generation, able to communicate with the given insurance product model. Therefore, this work compares and combines di erent code generation tools to accomplish the desired application generation. During this project, it is chosen an existing framework to create several software layers and respective components such as necessary classes to represent the Domain Model ; database mappings; Service layer; REST Application Program Interface (API); and a rich javascript-based presentation layer. As a conclusion, this project demonstrates that the proposed tool can generate the application already adapted and able to communicate with the provided conceptual model. Proving that this autonomous process is faster than the current manual development processes to adapt a presentation layer to an Insurance product model.O mercado segurador encontra-se dividido em várias linhas-de-negócio (e.g. Vida, Saúde, Propriedade) que têm naturalmente, diferentes modelos conceptuais para a representação dos seus produtos. Esta panóplia de modelos leva a uma dificuldade acrescida quando o software de camada de apresentação tem que ser constantemente adaptado aos novos modelos bem como ás alterações efetuadas aos modelos existentes. Com o intuito de suprimir esta constante adaptação a novos modelos, este trabalho visa a exploração e implementação de geradores de código de forma a permitir gerar toda uma aplicação que servirá de camada de apresentação ao utilizador para um dado modelo. Assim, este trabalho expõe e compara várias ferramentas de geração de código actualmente disponíveis, de forma a que seja escolhida a mais eficaz para responder aos objectivos estabelecidos. É então selecionada a ferramenta mais promissora e capaz de gerar vários componentes de software, gerando o seu modelo de domínio, mapeamento com as respectivas tabelas de base de dados, uma camada de lógica de negócio, serviços REST bem como uma camada de apresentação. Como conclusão, este trabalho apresenta uma solução que é capaz de se basear num modelo proveniente do sistema de modelação de produto e assim gerar completamente a aplicação de camada de apresentação desejada para esse mesmo modelo. Permitindo assim, um processo mais rápido e eficaz quando comparado com os processos manuais de desenvolvimento e de adaptação de código-fonte existentes

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Identifying Requirements in Microservice Architectural Systems

Microservices and microservice architecture has grown popularity and interest steadily since 2014 but many challenges are still faced in a software project when trying to adopt the concept. This work gathers challenges, possible solutions, and requirements related to the use of microservice architecture and therefore support the work of different stakeholders in a software project using microservice architecture, while also providing more information to the research as well. The study was conducted using systematic literature review (SLR). Overall, 63 scientific publications from four different scientific databases were selected and analysed. As a result, rapid evolution, life cycle management, complexity, performance, and a large number of integrations were identified as the most common challenges of microservice architecture. Solutions such as service orchestration, fog computing, decentralized data, and use of patterns were proposed to tackle these challenges. Regarding requirements, scalability, efficiency, flexibility, loose coupling, performance, and security appeared most frequently in the literature. The key finding of this work was the importance of data. How data acts as a base for functionalities and when inaccurate can cause complex challenges and make functionalities worthless. Based on this, we have a better understanding on what challenges may occur and what to focus on while working with microservice architecture in software development