<p>Software systems have grown larger and more complex in recent years. Generative software development strives to automate software development from a systems family by generating implementations using domain-specific languages. In current practice, specifying domain-specific languages is a manual task requiring expert analysis of multiple information sources. Furthermore, the concepts and relations represented in a language are grown through its usage. Keeping the language consistent with its usage is a time-consuming process requiring manual comparison between the language instances and its language specification. Feature model mining addresses these issues by synthesizing a representative model bottom-up from a sample set of instances called configurations.</p> <p>This thesis presents a mining algorithm that reverse-engineers a probabilistic feature model from a set of individual configurations. A configuration consists of a list of features that are defined as system properties that a stakeholder is interested in. Probabilistic expressions are retrieved from the sample configurations through the use of conjunctive and disjunctive association rule mining. These expressions are used to construct a probabilistic feature model. </p> <p>The mined feature model consists of a hierarchy of features, a set of additional hard constraints and soft constraints. The hierarchy describes the dependencies and alternative relations exhibited among the features. The additional hard constraints are a set of propositional formulas which must be satisfied in a legal configuration. Soft constraints describe likely defaults or common patterns.</p> <p>Systems families are often realized using object-oriented frameworks that provide reusable designs for constructing a family of applications. The mining algorithm is evaluated on a set of applications to retrieve a metamodel of the Java Applet framework. The feature model is then applied to the development of framework-specific modeling languages (FSMLs). FSMLs are domain-specific languages that model the framework-provided concepts and their rules for development.</p> <p>The work presented in this thesis provides the foundation for further research in feature model mining. The strengths and weaknesses of the algorithm are analyzed and the thesis concludes with a discussion of possible extensions.</p>

<p>Software systems have grown larger and more complex in recent years. Generative software development strives to automate software development from a systems family by generating implementations using domain-specific languages. In current practice, specifying domain-specific languages is a manual task requiring expert analysis of multiple information sources. Furthermore, the concepts and relations represented in a language are grown through its usage. Keeping the language consistent with its usage is a time-consuming process requiring manual comparison between the language instances and its language specification. Feature model mining addresses these issues by synthesizing a representative model bottom-up from a sample set of instances called configurations.</p> <p>This thesis presents a mining algorithm that reverse-engineers a probabilistic feature model from a set of individual configurations. A configuration consists of a list of features that are defined as system properties that a stakeholder is interested in. Probabilistic expressions are retrieved from the sample configurations through the use of conjunctive and disjunctive association rule mining. These expressions are used to construct a probabilistic feature model. </p> <p>The mined feature model consists of a hierarchy of features, a set of additional hard constraints and soft constraints. The hierarchy describes the dependencies and alternative relations exhibited among the features. The additional hard constraints are a set of propositional formulas which must be satisfied in a legal configuration. Soft constraints describe likely defaults or common patterns.</p> <p>Systems families are often realized using object-oriented frameworks that provide reusable designs for constructing a family of applications. The mining algorithm is evaluated on a set of applications to retrieve a metamodel of the Java Applet framework. The feature model is then applied to the development of framework-specific modeling languages (FSMLs). FSMLs are domain-specific languages that model the framework-provided concepts and their rules for development.</p> <p>The work presented in this thesis provides the foundation for further research in feature model mining. The strengths and weaknesses of the algorithm are analyzed and the thesis concludes with a discussion of possible extensions.</p>

Context: A common way to represent product lines is with variability modeling. Yet, there are different ways to extract and organize relevant characteristics of variability. Comprehensibility of these models and the ease of creating models are important for the efficiency of any variability management approach. Objective: The goal of this paper is to investigate the comprehensibility of two common styles to organize variability into models - hierarchical and constrained - where the dependencies between choices are specified either through the hierarchy of the model or as cross-cutting constraints, respectively. Method: We conducted a controlled experiment with a sample of 90 participants who were students with prior training in modeling. Each participant was provided with two variability models specified in Common Variability Language (CVL) and was asked to answer questions requiring interpretation of provided models. The models included 9 to 20 nodes and 8 to 19 edges and used the main variability elements. After answering the questions, the participants were asked to create a model based on a textual description. Results: The results indicate that the hierarchical modeling style was easier to comprehend from a subjective point of view, but there was also a significant interaction effect with the degree of dependency in the models, that influenced objective comprehension. With respect to model creation, we found that the use of a constrained modeling style resulted in higher correctness of variability models. Conclusions: Prior exposure to modeling style and the degree of dependency among elements in the model determine what modeling style a participant chose when creating the model from natural language descriptions. Participants tended to choose a hierarchical style for modeling situations with high dependency and a constrained style for situations with low dependency. Furthermore, the degree of dependency also influences the comprehension of the variability model.

Variability modeling is one of the key disciplines to cope with complex variability in large software product lines. It aims at creating, evolving, and configuring variability models, which describe the common and variable characteristics, also known as features, of products in a product line. Since the introduction of feature models more than twenty years ago, many variability modeling languages and notations have been proposed both in academia and industry, followed by hundreds of publications on variability modeling techniques that have built upon these theoretical foundations. Surprisingly, there are relatively few empirical studies that aim at understanding the use of such languages. What variability modeling concepts are actually used in practice? Do variability models applied in real-world look similar to those published in literature? In what technical and organizational contexts are variability models applicable? We present an empirical study that addresses this research gap. Our goals are i) to verify existing theoretical research, and ii) to explore real-world variability modeling languages and models expressed in them. We study concepts and semantics of variability modeling languages conceived by practitioners, and the usage of these concepts in real, large-scale models. Our aim is to support variability modeling research by providing empirical data about the use of its core modeling concepts, by identifying and characterizing further concepts that have not been as widely addressed, and by providing realistic assumptions about scale, structure, content, and complexity of real-world variability models. We believe that our findings are of relevance to variability modeling researchers and tool designers, for example, those working on interactive product configurators or feature dependency checkers. Our extracted models provide realistic benchmarks that can be used to evaluate new techniques. Recognizing the recent trend in software engineering to open up software platforms to facilitate inter-organizational reuse of software, we extend our empirical discourse to the emerging field of software ecosystems. As natural successors of successful product lines, ecosystems manage huge variability among and within their software assets, thus, represent a highly interesting class of systems to study variability modeling concepts and mechanisms. Our studied systems comprise eleven highly configurable software systems, two ecosystems with closed platforms, and three ecosystems relying on open platforms. Some of our subjects are among the largest successful systems in existence today. Results from a survey on industrial variability modeling complement these subjects. Our overall results provide empirical evidence that the well-researched concepts of feature modeling are used in practice, but also that more advanced concepts are needed. We observe that assumptions about variability models in the literature do not hold. Our study also reveals that variability models work best in centralized variability management scenarios, and that they are fragile and have to be controlled by a small team. We also identify a particular type of dependencies that is increasingly used in open platforms and helps sustain the growth of ecosystems. Interestingly, while enabling distributed variability, these dependencies rely on a centralized and stable vocabulary. Finally, we formulate new hypotheses and research questions that provide direction for future research.

Software product lines provide enhanced means for systematic reuse when constructing systems within a particular domain. In order to achieve this, systems in a product line are expected to have a significant amount of commonality. Variability is what distinguishes these systems from one another and is spread across various product line artifacts. This thesis focuses on modeling and managing product line variability. The concept of concerns is proposed as a means of variability modeling. Another proposal is related to the use of context free grammars to represent product line variability and to guarantee that any application derived according to the variability framework thus defined will be a valid one. This approach is evaluated for an example domain, in the light of novel evaluation criteria that are also introduced in the scope of this thesis.

Software product lines provide the opportunity to improve productivity, quality and time-to-market of software-based systems by means of systematic reuse. So as to accomplish systematic software reuse, elicitation of commonality knowledge is to be upheld by the analysis and management of variability knowledge inherent in domain requirements. Considerable effort is devoted to the management of functional variability, often neglecting the impact of quality concerns originating from non-functional requirements. In this thesis, a hybrid approach concentrating on the modeling of quantitative as well as qualitative concerns on quality has been proposed. This approach basically aims to support the domain design process by modeling non-functional variability. It further aims to support application design process by providing trade-off selection ability among quality concerns to control functional features that belong to the same domain. This approach is implemented and evaluated on an example domain to reveal its benefits on non-functional variability.

Aim of this thesis is to analyze the current situation, to identify bottlenecks and to propose improvements to the department of acquisitions in the selected financial company, which is an important part of the Czech market and which desire not to be named. In the first part of my thesis is provided to the reader a theoretical basis necessary to understand the following text. Then I create simulation models for individual teams, which take part in the mentioned department. These models are based only on average and total values. That is why in the next section, there is described the work to bring them closer to reality with shifts of administrators and the variable number of entities, which are processed throughout the day. Based on the analysis of calculated values, I propose two methods of redistribution of actions to reduce the number of employees and to increase their efficiency.

Les logiciels adaptatifs sont une classe de logiciels qui peuvent modifier leur structure et comportement à l'exécution afin de s'adapter à des nouveaux contextes d'exécution. Le développement de logiciels adaptatifs a été un domaine de recherche très actif les dix dernières années. Plusieurs approches utilisent des techniques issues des lignes des produits afin de développer de tels logiciels. Ils proposent des outils, des frameworks, ou des langages pour construire des architectures logicielles adaptatives, mais ne guident pas les ingénieurs dans leur utilisation. De plus, ils supposent que tous les éléments spécifiés à la conception sont disponibles dans l'architecture pour l'adaptation, même s'ils ne seront jamais utilisés. Ces éléments inutiles peuvent être une cause de soucis lors du déploiement sur une cible dont l'espace mémoire est très contraint par exemple. Par ailleurs, le remplacement de composants à l'exécution reste une tâche complexe, elle doit assurer non seulement la validité de la nouvelle version, mais aussi préserver la terminaison correcte des transactions en cours. Pour faire face à ces problèmes, cette thèse propose un processus de développement de logiciels adaptatifs où les tâches, les rôles, et les artefacts associés sont explicites. En particulier, le processus vise la spécification d'informations nécessaires pour construire des architectures logicielles adaptatives. Le résultat d'un tel processus est une architecture logicielle adaptative qui contient seulement des éléments utiles pour l'adaptation. De plus, un mécanisme d'adaptation est proposé basé sur la gestion de transactions pour assurer une adaptation dynamique cohérente. Elle assure la terminaison correcte des transactions en cours. Nous proposons pour cela la notion de dépendance transactionnelle : dépendance entre des actions réalisées par des composants différents. Nous proposons la spécification de ces dépendances dans le modèle de variabilité, et de l'exploiter pour décider des fonctions de contrôle dans les composants de l'architecture, des fonctions qui assurent une adaptation cohérente à l'exécution. / Adaptive software is a class of software which is able to modify its own internal structure and hence its behavior at runtime in response to changes in its operating environment. Adaptive software development has been an emerging research area of software engineering in the last decade. Many existing approaches use techniques issued from software product lines (SPLs) to develop adaptive software architectures. They propose tools, frameworks or languages to build adaptive software architectures but do not guide developers on the process of using them. Moreover, they suppose that all elements in the SPL specified are available in the architecture for adaptation. Therefore, the adaptive software architecture may embed unnecessary elements (components that will never be used) thus limiting the possible deployment targets. On the other hand, the components replacement at runtime remains a complex task since it must ensure the validity of the new version, in addition to preserving the correct completion of ongoing activities. To cope with these issues, this thesis proposes an adaptive software development process where tasks, roles, and associate artifacts are explicit. The process aims at specifying the necessary information for building adaptive software architectures. The result of such process is an adaptive software architecture that only contains necessary elements for adaptation. On the other hand, an adaptation mechanism is proposed based on transactions management for ensuring consistent dynamic adaptation. Such adaptation must guarantee the system state and ensure the correct completion of ongoing transactions. In particular, transactional dependencies are specified at design time in the variability model. Then, based on such dependencies, components in the architecture include the necessary mechanisms to manage transactions at runtime consistently.

Variability modeling is one of the key disciplines to cope with complex variability in large software product lines. It aims at creating, evolving, and configuring variability models, which describe the common and variable characteristics, also known as features, of products in a product line. Since the introduction of feature models more than twenty years ago, many variability modeling languages and notations have been proposed both in academia and industry, followed by hundreds of publications on variability modeling techniques that have built upon these theoretical foundations. Surprisingly, there are relatively few empirical studies that aim at understanding the use of such languages. What variability modeling concepts are actually used in practice? Do variability models applied in real-world look similar to those published in literature? In what technical and organizational contexts are variability models applicable? We present an empirical study that addresses this research gap. Our goals are i) to verify existing theoretical research, and ii) to explore real-world variability modeling languages and models expressed in them. We study concepts and semantics of variability modeling languages conceived by practitioners, and the usage of these concepts in real, large-scale models. Our aim is to support variability modeling research by providing empirical data about the use of its core modeling concepts, by identifying and characterizing further concepts that have not been as widely addressed, and by providing realistic assumptions about scale, structure, content, and complexity of real-world variability models. We believe that our findings are of relevance to variability modeling researchers and tool designers, for example, those working on interactive product configurators or feature dependency checkers. Our extracted models provide realistic benchmarks that can be used to evaluate new techniques. Recognizing the recent trend in software engineering to open up software platforms to facilitate inter-organizational reuse of software, we extend our empirical discourse to the emerging field of software ecosystems. As natural successors of successful product lines, ecosystems manage huge variability among and within their software assets, thus, represent a highly interesting class of systems to study variability modeling concepts and mechanisms. Our studied systems comprise eleven highly configurable software systems, two ecosystems with closed platforms, and three ecosystems relying on open platforms. Some of our subjects are among the largest successful systems in existence today. Results from a survey on industrial variability modeling complement these subjects. Our overall results provide empirical evidence that the well-researched concepts of feature modeling are used in practice, but also that more advanced concepts are needed. We observe that assumptions about variability models in the literature do not hold. Our study also reveals that variability models work best in centralized variability management scenarios, and that they are fragile and have to be controlled by a small team. We also identify a particular type of dependencies that is increasingly used in open platforms and helps sustain the growth of ecosystems. Interestingly, while enabling distributed variability, these dependencies rely on a centralized and stable vocabulary. Finally, we formulate new hypotheses and research questions that provide direction for future research.

Durant le développement de grandes lignes de produits logiciels, un ensemble de techniques d’implémentation traditionnelles, comme l’héritage ou les patrons de conception, est utilisé pour implémenter la variabilité. La notion de feature, en tant qu’unité réutilisable, n’a alors pas de représentation de première classe dans le code, et un choix inapproprié de techniques entraîne des incohérences entre variabilités du domaine et de l’implémentation. Dans cette thèse, nous étudions la diversité de la majorité des techniques d’implémentation de la variabilité, que nous organisons dans un catalogue étendu. Nous proposons un framework pour capturer et modéliser, de façon fragmentée, dans des modèles techniques de variabilité, la variabilité implémentée par plusieurs techniques combinées. Ces modèles utilisent les points de variation et les variantes, avec leur relation logique et leur moment de résolution, pour abstraire les techniques d’implémentation. Nous montrons comment étendre le framework pour obtenir la traçabilité de feature avec leurs implémentations respectives. De plus, nous fournissons une approche outillée pour vérifier la cohérence de la variabilité implémentée. Notre méthode utilise du slicing pour vérifier partiellement les formules de logique propositionnelles correspondantes aux deux niveaux dans le cas de correspondence 1–m entre ces niveaux. Ceci permet d’obtenir une détection automatique et anticipée des incohérences. Concernant la validation, le framework et la méthode de vérification ont été implémentés en Scala. Ces implémentations ont été appliquées à un vrai système hautement variable et à trois études de cas de lignes de produits. / When large software product lines are engineered, a combined set of traditional techniques, such as inheritance, or design patterns, is likely to be used for implementing variability. In these techniques, the concept of feature, as a reusable unit, does not have a first-class representation at the implementation level. Further, an inappropriate choice of techniques becomes the source of variability inconsistencies between the domain and the implemented variabilities. In this thesis, we study the diversity of the majority of variability implementation techniques and provide a catalog that covers an enriched set of them. Then, we propose a framework to explicitly capture and model, in a fragmented way, the variability implemented by several combined techniques into technical variability models. These models use variation points and variants, with their logical relation and binding time, to abstract the implementation techniques. We show how to extend the framework to trace features with their respective implementation. In addition, we use this framework and provide a tooled approach to check the consistency of the implemented variability. Our method uses slicing to partially check the corresponding propositional formulas at the domain and implementation levels in case of 1–to–m mapping. It offers an early and automatic detection of inconsistencies. As validation, we report on the implementation in Scala of the framework as an internal domain specific language, and of the consistency checking method. These implementations have been applied on a real feature-rich system and on three product line case studies, showing the feasibility of the proposed contributions.