An approach for clone detection in documentation reuse

The paper focuses on the searching method for repetitions in DocBook/DRL or plain text documents. An algorithm has been designed based on software clone detection. The algorithm supports filtering results: clones are rejected if clone length in the group is less than 5 symbols, intersection of clone groups is eliminated, meaningfulness clones are removed, the groups containing clones consisting only of XML are eliminated. Remaining search is supported: found clones are extracted from the documentation, and clone search is repeated. One step is proved to be enough. Adaptive reuse technique of Paul Bassett – Stan Jarzabek has been implemented. A software tool has been developed on the basis of the algorithm. The tool supports setting parameters for repetitions detection and visualization of the obtained results. The tool is integrated into DocLine document development environment, and provides refactoring of documents using found clones. The Clone Miner clone detection utility is used for clones search. The method has been evaluated for Linux Kernel Documentation (29 documents, 25000 lines). Five semantic kinds of clones have been selected: terms (abbreviations, one word and two word terms), hyperlinks, license agreements, functionality description, and code examples. 451 meaningful clone groups have been found, average clone length is 4.43 tokens, and average number of clones in a group is 3.56

Variability management in a model-driven software product line

Variability management in Software Product Lines (SPLs) has two fundamental challenges: (1) the expression of common and variable features, and (2) the development of applications employing properly such features. In this paper, we present a Software Product Line based on Models (MD-SPL). We separate the concepts related to SPLs in different domains and we build core assets like feature models, metamodels, and three different types of transformation rules to transform models from a source domain to different (variable) models into a target domain. By using transformation rules, we are able to generate applications in an incremental process, guided by a set of features selected for each target domain. Thus, we manage to extend the SPLs scope, separate the domains diminishing the complexity to create applications with variable characteristics, and automatically generate applications using transformation rules. In order to illustrate our approach, we have built a MDSPL where the products are small applications used in programming computers teaching

Understanding Variability-Aware Analysis in Low-Maturity Variant-Rich Systems

Context: Software systems often exist in many variants to support varying stakeholder requirements, such as specific market segments or hardware constraints. Systems with many variants (a.k.a. variant-rich systems) are highly complex due to the variability introduced to support customization. As such, assuring the quality of these systems is also challenging since traditional single-system analysis techniques do not scale when applied. To tackle this complexity, several variability-aware analysis techniques have been conceived in the last two decades to assure the quality of a branch of variant-rich systems called software product lines. Unfortunately, these techniques find little application in practice since many organizations do use product-line engineering techniques, but instead rely on low-maturity \clo~strategies to manage their software variants. For instance, to perform an analysis that checks that all possible variants that can be configured by customers (or vendors) in a car personalization system conform to specified performance requirements, an organization needs to explicitly model system variability. However, in low-maturity variant-rich systems, this and similar kinds of analyses are challenging to perform due to (i) immature architectures that do not systematically account for variability, (ii) redundancy that is not exploited to reduce analysis effort, and (iii) missing essential meta-information, such as relationships between features and their implementation in source code.Objective: The overarching goal of the PhD is to facilitate quality assurance in low-maturity variant-rich systems. Consequently, in the first part of the PhD (comprising this thesis) we focus on gaining a better understanding of quality assurance needs in such systems and of their properties.Method: Our objectives are met by means of (i) knowledge-seeking research through case studies of open-source systems as well as surveys and interviews with practitioners; and (ii) solution-seeking research through the implementation and systematic evaluation of a recommender system that supports recording the information necessary for quality assurance in low-maturity variant-rich systems. With the former, we investigate, among other things, industrial needs and practices for analyzing variant-rich systems; and with the latter, we seek to understand how to obtain information necessary to leverage variability-aware analyses.Results: Four main results emerge from this thesis: first, we present the state-of-practice in assuring the quality of variant-rich systems, second, we present our empirical understanding of features and their characteristics, including information sources for locating them; third, we present our understanding of how best developers\u27 proactive feature location activities can be supported during development; and lastly, we present our understanding of how features are used in the code of non-modular variant-rich systems, taking the case of feature scattering in the Linux kernel.Future work: In the second part of the PhD, we will focus on processes for adapting variability-aware analyses to low-maturity variant-rich systems.Keywords:\ua0Variant-rich Systems, Quality Assurance, Low Maturity Software Systems, Recommender Syste

Feature-Oriented Variability Management in Product Line Engineering

Features are effective communication “media” among different stakeholders of products or product lines. Hence, feature orientation in analyzing com-monality and variability of product lines is appealing. In this paper, we introduce a feature-oriented ap-proach to explicit modeling and managing variability information of a product line. It takes features as first class entities for controlling variability and enables an organization to exploit commonality and manage vari-ability in both problem and solution space

Modelagem de características para representar informações de contexto relevantes em um domínio de aplicação sensível a mudanças em tempo de execução

Trabalho de Conclusão de Curso, apresentado para obtenção do grau de bacharel no curso de Ciência da Computação da Universidade do Extremo Sul Catarinense, UNESC.Este estudo trata da modelagem de características para representar informações de contexto em um domínio de aplicação sensível. O assunto é relevante, pelo fato de discutir um tema em evidência na era da computação móvel. Com o aumento da utilização de dispositivos móveis, surge também diferentes aplicações computacionais com a necessidade de realizar tarefas complexas e processar grande volume de informação em pouco tempo. Devido a esta necessidade, surgem desafios de interpretação e uso das informações para desenvolvimento de aplicações sensíveis ao contexto. Portanto, o trabalho tem o objetivo de desenvolver um modelo de características para representar informações de contexto relevantes em um domínio de aplicação sensível às mudanças em tempo de execução. A metodologia utilizada é a revisão bibliográfica de obras publicadas entre os anos de 1997 à 2017 a fim de se obter um embasamento teórico necessário sobre computação sensível ao contexto e alguns exemplos de aplicações que utilizam este conceito. Também se enfatiza o estudo de modelagem de contexto, o levantamento de informações para modelagem de características e a variabilidade, apresenta os trabalhos correlatos, enfatizando a experiência do usuário em sistemas computacionais e se versa sobre a elaboração do modelo que foi realizado, a estruturação das informações contextuais, apontando a metodologia utilizada junto ao cronograma. O trabalho conclui que no desenvolvimento de uma nova aplicação, existe uma alta expectativa de reusabilidade, implicando em redução de tempo e custo de projeto, sem queda na qualidade dos trabalhos

Industrialising Software Development in Systems Integration

Compared to other disciplines, software engineering as of today is still dependent on craftsmanship of highly-skilled workers. However, with constantly increasing complexity and efforts, existing software engineering approaches appear more and more inefficient. A paradigm shift towards industrial production methods seems inevitable. Recent advances in academia and practice have lead to the availability of industrial key principles in software development as well. Specialization is represented in software product lines, standardization and systematic reuse are available with component-based development, and automation has become accessible through model-driven engineering. While each of the above is well researched in theory, only few cases of successful implementation in the industry are known. This becomes even more evident in specialized areas of software engineering such as systems integration. Today’s IT systems need to quickly adapt to new business requirements due to mergers and acquisitions and cooperations between enterprises. This certainly leads to integration efforts, i.e. joining different subsystems into a cohesive whole in order to provide new functionality. In such an environment. the application of industrial methods for software development seems even more important. Unfortunately, software development in this field is a highly complex and heterogeneous undertaking, as IT environments differ from customer to customer. In such settings, existing industrialization concepts would never break even due to one-time projects and thus insufficient economies of scale and scope. This present thesis, therefore, describes a novel approach for a more efficient implementation of prior key principles while considering the characteristics of software development for systems integration. After identifying the characteristics of the field and their affects on currently-known industrialization concepts, an organizational model for industrialized systems integration has thus been developed. It takes software product lines and adapts them in a way feasible for a systems integrator active in several business domains. The result is a three-tiered model consolidating recurring activities and reducing the efforts for individual product lines. For the implementation of component-based development, the present thesis assesses current component approaches and applies an integration metamodel to the most suitable one. This ensures a common understanding of systems integration across different product lines and thus alleviates component reuse, even across product line boundaries. The approach is furthermore aligned with the organizational model to depict in which way component-based development may be applied in industrialized systems integration. Automating software development in systems integration with model-driven engineering was found to be insufficient in its current state. The reason herefore lies in insufficient tool chains and a lack of modelling standards. As an alternative, an XML-based configuration of products within a software product line has been developed. It models a product line and its products with the help of a domain-specific language and utilizes stylesheet transformations to generate compliable artefacts. The approach has been tested for its feasibility within an exemplarily implementation following a real-world scenario. As not all aspects of industrialized systems integration could be simulated in a laboratory environment, the concept was furthermore validated during several expert interviews with industry representatives. Here, it was also possible to assess cultural and economic aspects. The thesis concludes with a detailed summary of the contributions to the field and suggests further areas of research in the context of industrialized systems integration

Efficient Reasoning Techniques for Large Scale Feature Models

In Software Product Lines (SPLs), a feature model can be used to represent the similarities and differences within a family of software systems. This allows describing the systems derived from the product line as a unique combination of the features in the model. What makes feature models particularly appealing is the fact that the constraints in the model prevent incompatible features from being part of the same product. Despite the benefits of feature models, constructing and maintaining these models can be a laborious task especially in product lines with a large number of features and constraints. As a result, the study of automated techniques to reason on feature models has become an important research topic in the SPL community in recent years. Two techniques, in particular, have significant appeal for researchers: SAT solvers and Binary Decision Diagrams (BDDs). Each technique has been applied successfully for over four decades now to tackle many practical combinatorial problems in various domains. Currently, several approaches have proposed the compilation of feature models to specific logic representations to enable the use of SAT solvers and BDDs. In this thesis, we argue that several critical issues related to the use of SAT solvers and BDDs have been consistently neglected. For instance, satisfiability is a well-known NP-complete problem which means that, in theory, a SAT solver might be unable to check the satisfiability of a feature model in a feasible amount of time. Similarly, it is widely known that the size of BDDs can become intractable for large models. At the same time, we currently do not know precisely whether these are real issues when feature models, especially large ones, are compiled to SAT and BDD representations. Therefore, in our research we provide a significant step forward in the state-of-the-art by examining deeply many relevant properties of the feature modeling domain and the mechanics of SAT solvers and BDDs and the sensitive issues related to these techniques when applied in that domain. Specifically, we provide more accurate explanations for the space and/or time (in)tractability of these techniques in the feature modeling domain, and enhance the algorithmic performance of these techniques for reasoning on feature models. The contributions of our work include the proposal of novel heuristics to reduce the size of BDDs compiled from feature models, several insights on the construction of efficient domain-specific reasoning algorithms for feature models, and empirical studies to evaluate the efficiency of SAT solvers in handling very large feature models