Annotating Software Documentation in Semantic Wikis

The use of software documentation in traditional file-based format has various issues. Storing and annotating the content of software documentation in semantic wiki pages addresses many of these issues and supports knowledge retrieval. This paper reports on the application of semantic annotation and knowledge retrieval using the ArchiMind semantic wiki system. Knowledge in documentation is annotated by indexing text with a lightweight software engineering ontology. The process and the use-cases of this semantic annotation of software documents are described

A Query Language for Software Architecture Information (Extended version)

Software maintenance is an important part of a software system's life cycle. Maintenance tasks of existing software systems suffer from architecture information that is diverging over time (architectural drift). The Digital Architecture Twin (DArT) can support software maintenance by providing up-to-date architecture information. For this, the DArT gathers such information and co-evolves with a software system, enabling continuous reverse engineering. But the crucial link for stakeholders to retrieve this information is missing. To fill this gap, we contribute the Architecture Information Query Language (AIQL), which enables stakeholders to access up-to-date and tailored architecture information. We derived four application scenarios in the context of continuous reverse engineering. We showed that the AIQL provides the required functionality to formulate queries for the application scenarios and that the language scales for use with real-world software systems. In a user study, stakeholders agreed that the language is easy to understand and assessed its value to the specific stakeholder for the application scenarios

Model-to-Code transformation from product-line architecture models to aspectJ

Software Product Line Engineering has significant advantages in family-based software development. The common and variable structure for all products of a family is defined through a Product-Line Architecture (PLA) that consists of a common set of reusable components and connectors which can be configured to build the different products. The design of PLA requires solutions for capturing such configuration (variability). The Flexible-PLA Model is a solution that supports the specification of external variability of the PLA configuration, as well as internal variability of components. However, a complete support for product-line development requires translating architecture specifications into code. This complex task needs automation to avoid human error. Since Model-Driven Development allows automatic code generation from models, this paper presents a solution to automatically generate AspectJ code from Flexible-PLA models previously configured to derive specific products. This solution is supported by a modeling framework and validated in a software factory

A Comparative Study on Model-Driven Requirements Engineering for Software Product Lines

[EN] Model-Driven Engineering (MDE) and Software Product Lines (SPL) are two software development paradigms that emphasize reusing. The former reuse domain knowledge is represented as models and model transformations for product development, and the latter reuse domain knowledge is represented as core assets to produce a family of products in a given domain. The adequate combination of both paradigms can bring together important advantages to the software development community. However, how to manage requirements during a model-driven product line development remains an open challenge. In particular, the Requirements Engineering (RE) activity must deal with specific properties such as variability and commonality for a whole family of products. This paper presents a comparative study of eleven approaches that perform a MDE strategy in the RE activity for SPL, with the aim of identify ing current practices and research gaps. In summary, most of the approaches are focused on the Domain Engineering phase of the SPL development, giving less attention to the Application Engineering phase. Moreover there is a lack of coverage of the Scoping activity, which defines the SPL boundaries. Several approaches apply some model transformations to obtain architectural and application requirements artifacts. Regarding the tool support for requirements specification and management, we found that most of the approaches use only academic prototypes. Regarding the validation of the approaches, the use of Case Studies as a proof of concept was the most commonly used method; however, there is a lack of well-defined case studies and empirical studies to improve the proposals.This research is part of the MULTIPLE project (with ref. TIN2009-13838).Blanes Domínguez, D.; Insfrán Pelozo, CE. (2012). A Comparative Study on Model-Driven Requirements Engineering for Software Product Lines. Revista de Sistemas e Computação. 2(1):3-13. http://hdl.handle.net/10251/43841S3132

Supporting the grow-and-prune model for evolving software product lines

207 p.Software Product Lines (SPLs) aim at supporting the development of a whole family of software products through a systematic reuse of shared assets. To this end, SPL development is separated into two interrelated processes: (1) domain engineering (DE), where the scope and variability of the system is defined and reusable core-assets are developed; and (2) application engineering (AE), where products are derived by selecting core assets and resolving variability. Evolution in SPLs is considered to be more challenging than in traditional systems, as both core-assets and products need to co-evolve. The so-called grow-and-prune model has proven great flexibility to incrementally evolve an SPL by letting the products grow, and later prune the product functionalities deemed useful by refactoring and merging them back to the reusable SPL core-asset base. This Thesis aims at supporting the grow-and-prune model as for initiating and enacting the pruning. Initiating the pruning requires SPL engineers to conduct customization analysis, i.e. analyzing how products have changed the core-assets. Customization analysis aims at identifying interesting product customizations to be ported to the core-asset base. However, existing tools do not fulfill engineers needs to conduct this practice. To address this issue, this Thesis elaborates on the SPL engineers' needs when conducting customization analysis, and proposes a data-warehouse approach to help SPL engineers on the analysis. Once the interesting customizations have been identified, the pruning needs to be enacted. This means that product code needs to be ported to the core-asset realm, while products are upgraded with newer functionalities and bug-fixes available in newer core-asset releases. Herein, synchronizing both parties through sync paths is required. However, the state of-the-art tools are not tailored to SPL sync paths, and this hinders synchronizing core-assets and products. To address this issue, this Thesis proposes to leverage existing Version Control Systems (i.e. git/Github) to provide sync operations as first-class construct

Conception architecturale des systèmes robotiques orientée services

Robotics has experienced an increasing evolution and interest from the society in recent years. Robots are no longer produced exclusively to perform repetitive tasks in factories, they have been designed to collaborate with humans in several important application domains. Robotic systems that control these robots are therefore becoming larger, more complex, and difficult to develop. In this scenario, Service-Oriented Architecture (SOA) has been investigated as a promising architectural style for the design of robotic systems in a flexible, reusable, and productive manner. Despite the existence of a considerable amount of Service-Oriented Robotic Systems (SORS), most of them have been developed in an ad hoc manner. The little attention and limited support devoted to the design of SORS software architectures may not only hamper the benefits of SOA adoption, but also reduce the overall quality of robotic systems, which are often used in safety-critical contexts. This thesis aims at improving the understanding and systematization of SORS architectural design.La Robotique a connu une évolution remarquable au cours des dernières années, couplée à un intérêt croissant de la société pour ce domaine. Les robots ne sont plus fabriqués exclusivement pour effectuer des tâches répétitives dans les usines, mais ils sont aussi créés pour collaborer avec les humains dans plusieurs domaines d'application d'importance. Les systèmes robotiques qui contrôlent ces robots sont donc de plus en plus larges, complexes et difficiles à développer. Dans ce contexte, l'Architecture Orientée Services (SOA) a été identifiée comme un style d'architecture logicielle prometteur pour concevoir des systèmes robotiques de manière flexible, réutilisable et productive. Cependant, malgré le nombre considérable de Systèmes Robotiques Orientées Services (SORS) existants aujourd'hui, la plupart d'entre eux ont été développés de manière ad hoc. Le peu d'attention et le soutien limité portés à la conception d'architectures logicielles SORS peuvent non seulement masquer les avantages de l'adoption de la SOA, mais aussi réduire la qualité globale des systèmes robotiques, qui sont souvent utilisés dans des contextes de sécurité critiques. Cette thèse vise à améliorer la compréhension et la systématisation de la conception architecturale SORS. Elle décrit une taxonomie des services pour le domaine de la robotique, puis propose un processus ainsi qu'une architecture de référence afin de systématiser la conception d'architectures logicielles SORS. Les résultats obtenus dans les études d'évaluation montrent qu'à la fois le processus et l'architecture de référence peuvent avoir un impact positif sur la qualité des architectures logicielles SORS et, par conséquent, contribuent à l'amélioration des systèmes robotique

Supporting the grow-and-prune model for evolving software product lines

207 p.Software Product Lines (SPLs) aim at supporting the development of a whole family of software products through a systematic reuse of shared assets. To this end, SPL development is separated into two interrelated processes: (1) domain engineering (DE), where the scope and variability of the system is defined and reusable core-assets are developed; and (2) application engineering (AE), where products are derived by selecting core assets and resolving variability. Evolution in SPLs is considered to be more challenging than in traditional systems, as both core-assets and products need to co-evolve. The so-called grow-and-prune model has proven great flexibility to incrementally evolve an SPL by letting the products grow, and later prune the product functionalities deemed useful by refactoring and merging them back to the reusable SPL core-asset base. This Thesis aims at supporting the grow-and-prune model as for initiating and enacting the pruning. Initiating the pruning requires SPL engineers to conduct customization analysis, i.e. analyzing how products have changed the core-assets. Customization analysis aims at identifying interesting product customizations to be ported to the core-asset base. However, existing tools do not fulfill engineers needs to conduct this practice. To address this issue, this Thesis elaborates on the SPL engineers' needs when conducting customization analysis, and proposes a data-warehouse approach to help SPL engineers on the analysis. Once the interesting customizations have been identified, the pruning needs to be enacted. This means that product code needs to be ported to the core-asset realm, while products are upgraded with newer functionalities and bug-fixes available in newer core-asset releases. Herein, synchronizing both parties through sync paths is required. However, the state of-the-art tools are not tailored to SPL sync paths, and this hinders synchronizing core-assets and products. To address this issue, this Thesis proposes to leverage existing Version Control Systems (i.e. git/Github) to provide sync operations as first-class construct

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

This thesis presents an approach for the design time analysis of energy efficiency for static and self-adaptive software systems. The quality characteristics of a software system, such as performance and operating costs, strongly depend upon its architecture. Software architecture is a high-level view on software artifacts that reflects essential quality characteristics of a system under design. Design decisions made on an architectural level have a decisive impact on the quality of a system. Revising architectural design decisions late into development requires significant effort. Architectural analyses allow software architects to reason about the impact of design decisions on quality, based on an architectural description of the system. An essential quality goal is the reduction of cost while maintaining other quality goals. Power consumption accounts for a significant part of the Total Cost of Ownership (TCO) of data centers. In 2010, data centers contributed 1.3% of the world-wide power consumption. However, reasoning on the energy efficiency of software systems is excluded from the systematic analysis of software architectures at design time. Energy efficiency can only be evaluated once the system is deployed and operational. One approach to reduce power consumption or cost is the introduction of self-adaptivity to a software system. Self-adaptive software systems execute adaptations to provision costly resources dependent on user load. The execution of reconfigurations can increase energy efficiency and reduce cost. If performed improperly, however, the additional resources required to execute a reconfiguration may exceed their positive effect. Existing architecture-level energy analysis approaches offer limited accuracy or only consider a limited set of system features, e.g., the used communication style. Predictive approaches from the embedded systems and Cloud Computing domain operate on an abstraction that is not suited for architectural analysis. The execution of adaptations can consume additional resources. The additional consumption can reduce performance and energy efficiency. Design time quality analyses for self-adaptive software systems ignore this transient effect of adaptations. This thesis makes the following contributions to enable the systematic consideration of energy efficiency in the architectural design of self-adaptive software systems: First, it presents a modeling language that captures power consumption characteristics on an architectural abstraction level. Second, it introduces an energy efficiency analysis approach that uses instances of our power consumption modeling language in combination with existing performance analyses for architecture models. The developed analysis supports reasoning on energy efficiency for static and self-adaptive software systems. Third, to ease the specification of power consumption characteristics, we provide a method for extracting power models for server environments. The method encompasses an automated profiling of servers based on a set of restrictions defined by the user. A model training framework extracts a set of power models specified in our modeling language from the resulting profile. The method ranks the trained power models based on their predicted accuracy. Lastly, this thesis introduces a systematic modeling and analysis approach for considering transient effects in design time quality analyses. The approach explicitly models inter-dependencies between reconfigurations, performance and power consumption. We provide a formalization of the execution semantics of the model. Additionally, we discuss how our approach can be integrated with existing quality analyses of self-adaptive software systems. We validated the accuracy, applicability, and appropriateness of our approach in a variety of case studies. The first two case studies investigated the accuracy and appropriateness of our modeling and analysis approach. The first study evaluated the impact of design decisions on the energy efficiency of a media hosting application. The energy consumption predictions achieved an absolute error lower than 5.5% across different user loads. Our approach predicted the relative impact of the design decision on energy efficiency with an error of less than 18.94%. The second case study used two variants of the Spring-based community case study system PetClinic. The case study complements the accuracy and appropriateness evaluation of our modeling and analysis approach. We were able to predict the energy consumption of both variants with an absolute error of no more than 2.38%. In contrast to the first case study, we derived all models automatically, using our power model extraction framework, as well as an extraction framework for performance models. The third case study applied our model-based prediction to evaluate the effect of different self-adaptation algorithms on energy efficiency. It involved scientific workloads executed in a virtualized environment. Our approach predicted the energy consumption with an error below 7.1%, even though we used coarse grained measurement data of low accuracy to train the input models. The fourth case study evaluated the appropriateness and accuracy of the automated model extraction method using a set of Big Data and enterprise workloads. Our method produced power models with prediction errors below 5.9%. A secondary study evaluated the accuracy of extracted power models for different Virtual Machine (VM) migration scenarios. The results of the fifth case study showed that our approach for modeling transient effects improved the prediction accuracy for a horizontally scaling application. Leveraging the improved accuracy, we were able to identify design deficiencies of the application that otherwise would have remained unnoticed

Modelling Event-Based Interactions in Component-Based Architectures for Quantitative System Evaluation

This dissertation thesis presents an approach enabling the modelling and quality-of-service prediction of event-based systems at the architecture-level. Applying a two-step model refinement transformation, the approach integrates platform-specific performance influences of the underlying middleware while enabling the use of different existing analytical and simulation-based prediction techniques