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Automated reasoning on feature models

Software Product Line (SPL) Engineering has proved to be an effective method for software production. However, in the SPL community it is well recognized that variability in SPLs is increasing by the thousands. Hence, an automatic support is needed to deal with variability in SPL. Most of the current proposals for automatic reasoning on SPL are not devised to cope with extra–functional features. In this paper we introduce a proposal to model and reason on an SPL using constraint programming. We take into account functional and extra–functional features, improve current proposals and present a running, yet feasible implementation.

Automated Diagnosis of Product-Line Configuration Errors in Feature Models

Feature models are widely used to model software product-line (SPL) variability. SPL variants are configured by selecting feature sets that satisfy feature model constraints. Configuration of large feature models can involve multiple stages and participants, which makes it hard to avoid conflicts and errors. New techniques are therefore needed to debug invalid configurations and derive the minimal set of changes to fix flawed configurations. This paper provides three contributions to debugging feature model configurations: (1) we present a technique for transforming a flawed feature model configuration into a constraint satisfaction problem (CSP) and show how a constraint solver can derive the minimal set of feature selection changes to fix an invalid configuration, (2) we show how this diagnosis CSP can automatically resolve conflicts between configuration participant decisions, and (3) we present experiment results that evaluate our technique. These results show that our technique scales to models with over 5,000 features, which is well beyond the size used to validate other automated techniques.

Automated error analysis for the agilization of feature modeling

Software Product Lines (SPL) and agile methods share the common goal of rapidly developing high-quality software. Although they follow different approaches to achieve it, some synergies can be found between them by (i) applying agile techniques to SPL activities so SPL development becomes more agile; and (ii) tailoring agile methodologies to support the development of SPL. Both options require an intensive use of feature models, which are usually strongly affected by changes on requirements. Changing large-scale feature models as a consequence of changes on requirements is a well-known error-prone activity. Since one of the objectives of agile methods is a rapid response to changes in requirements, it is essential an automated error analysis support in order to make SPL development more agile and to produce error-free feature models. As a contribution to find the intended synergies, this article sets the basis to provide an automated support to feature model error analysis by means of a framework which is organized in three levels: a feature model level, where the problem of error treatment is described; a diagnosis level, where an abstract solution that relies on Reiters theory of diagnosis is proposed; and an implementation level, where the abstract solution is implemented by using Constraint Satisfaction Problems (CSP). To show an application of our proposal, a real case study is presented where the Feature-Driven Development (FDD) methodology is adapted to develop an SPL. Current proposals on error analysis are also studied and a comparison among them and our proposal is provided. Lastly, the support of new kinds of errors and different implementation levels for the proposed framework are proposed as the focus of our future work.

FAMA Framework

FAMA framework (FAMA FW) is a tool for the automated analysis of variability models (VM). Its main objective is providing an extensible framework where current research on VM automated analysis might be developed and easily integrated into a final product. FAMA FW is built following the SPL paradigm supporting different variability metamodels, reasoners or solvers, analysis questions and reasoner selectors, easing the production of customized VM analysis tools. FAMA FW is written in Java and distributed under LGPL License.

An overview of Dynamic Software Product Line architectures and techniques: Observations from research and industry

Over the last two decades, software product lines have been used successfully in industry for building families of systems of related products, maximizing reuse, and exploiting their variable and configurable options. In a changing world, modern software demands more and more adaptive features, many of them performed dynamically, and the requirements on the software architecture to support adaptation capabilities of systems are increasing in importance. Today, many embedded system families and application domains such as ecosystems, service-based applications, and self-adaptive systems demand runtime capabilities for flexible adaptation, reconfiguration, and post-deployment activities. However, as traditional software product line architectures fail to provide mechanisms for runtime adaptation and behavior of products, there is a shift toward designing more dynamic software architectures and building more adaptable software able to handle autonomous decision-making, according to varying conditions. Recent development approaches such as Dynamic Software Product Lines (DSPLs) attempt to face the challenges of the dynamic conditions of such systems but the state of these solution architectures is still immature. In order to provide a more comprehensive treatment of DSPL models and their solution architectures, in this research work we provide an overview of the state of the art and current techniques that, partially, attempt to face the many challenges of runtime variability mechanisms in the context of Dynamic Software Product Lines. We also provide an integrated view of the challenges and solutions that are necessary to support runtime variability mechanisms in DSPL models and software architectures.

Using java CSP solvers in the automated analyses of feature models

Feature Models are used in different stages of software development and are recognized to be an important asset in model transformation techniques and software product line development. The automated analysis of feature models is being recognized as one of the key challenges for automated software development in the context of Software Product Lines. In our previous work we explained how a feature model can be transformed into a constraint satisfaction problem. However cardinalities were not considered. In this paper we present how a cardinality-based feature model can be also translated into a constraint satisfaction problem. In that connection, it is possible to use off-the-shelf tools to automatically accomplish several tasks such as calculating the number of possible feature configurations and detecting possible conflicts. In addition, we present a performance test between two off-the-shelf Java constraint solvers. To the best of our knowledge, this is the first time a performance test is presented using solvers for feature modelling proposes

Automated Merging of Feature Models Using Graph Transformations

Feature Models (FMs) are a key artifact for variability and commonality management in Software Product Lines (SPLs). In this context, the merging of FMs is being recognized as an important operation to support the adoption and evolution of SPLs. However, providing automated support for merging FMs still remains an open challenge. In this paper, we propose using graph transformations as a suitable technology and associated formalism to automate the merging of FMs. In particular, we first present a catalogue of technology-independent visual rules to describe how to merge FMs. Next, we propose a prototype implementation of our catalogue using the AGG system. Finally, we show the feasibility of our proposal by means of a running example inspired by the mobile phone industry. To the best of our knowledge, this is the first approach providing automated support for merging FMs including feature attributes and cross-tree constraints.

Automated configuration support for infrastructure migration to the cloud

With an increasing number of cloud computing offerings in the market, migrating an existing computational infrastructure to the cloud requires comparison of different offers in order to find the most suitable configuration. Cloud providers offer many configuration options, such as location, purchasing mode, redundancy, and extra storage. Often, the information about such options is not well organised. This leads to large and unstructured configuration spaces, and turns the comparison into a tedious, error-prone search problem for the customers. In this work we focus on supporting customer decision making for selecting the most suitable cloud configuration-in terms of infrastructural requirements and cost. We achieve this by means of variability modelling and analysis techniques. Firstly, we structure the configuration space of an IaaS using feature models, usually employed for the modelling of variability-intensive systems, and present the case study of the Amazon EC2. Secondly, we assist the configuration search process. Feature models enable the use of different analysis operations that, among others, automate the search of optimal configurations. Results of our analysis show how our approach, with a negligible analysis time, outperforms commercial approaches in terms of expressiveness and accuracy. We support the decision making in migration planning to the cloud.We use Feature Models to describe the configuration space of an IaaS.We automate the search of the most suitable IaaS configuration.Our approach improves the results of commercial applications on Amazon EC2.

Migrating to the Cloud - A Software Product Line based Analysis

Identifying which part of a local system should be migrated to a public Cloud environment is often a difficult and error prone process. With the significant (and increasing) number of commercial Cloud providers, choosing a provider whose capability best meets requirements is also often difficult. Most Cloud service providers offer large amounts of configurable resources, which can be combined in a number of different ways. In the case of small and medium companies, finding a suitable configuration with the minimum cost is often an essential requirement to migrate, or even to initiate the decision process for migration. We interpret this need as a problem associated with variability management and analysis. Variability techniques and models deal with large configuration spaces, and have been proposed previously to support configuration processes in industrial cases. Furthermore, this is a mature field which has a large catalog of analysis operations to extract valuable information in an automated way. Some of these operations can be used and tailored for Cloud environments. We focus in this work on Amazon Cloud services, primarily due to the large number of possible configurations available by this service provider and its popularity. Our approach can also be adapted to other providers offering similar capabilities.

Consistency maintenance for evolving feature models

Software product line (SPL) techniques handle the construction of customized systems. One of the most common representations of the decisions a customer can make in SPLs is feature models (FMs). An FM represents the relationships among common and variable features in an SPL. Features are a representation of the characteristics in a system that are relevant to customers. FMs are subject to change since the set of features and their relationships can change along an SPL lifecycle. Due to this evolution, the consistency of FMs may be compromised. There exist some approaches to detect and explain inconsistencies in FMs, however this process can take a long time for large FMs. In this paper we present a complementary approach to dealing with inconsistencies in FM evolution scenarios that improves the performance for existing approaches reducing the impact of change to the smallest part of an FM that changes. To achieve our goal, we formalize FMs from an ontological perspective and define constraints that must be satisfied in FMs to be consistent. We define a set of primitive operations that modify FMs and which are responsible for the FM evolution, analyzing their impact on the FM consistency. We propose a set of predefined strategies to keep the consistency for error-prone operations. As a proof-of-concept we present the results of our experiments, where we check for the effectiveness and efficiency of our approach in FMs with thousands of features. Although our approach is limited by the kinds of consistency constraints and the primitive operations we define, the experiments present a significant improvement in performance results in those cases where they are applicable.