Three-Dimensional Feature Diagrams Visualization

Visualizing and manipulating large feature diagrams is still an open issue for the SPL community. Few effort has been made on improving the techniques to get the most out of drawing space and current feature modeling tools either use file-system-like trees or 2D graphs that must be scrolled to locate features. The aim of this paper is presenting a new method to draw large feature models based on cone trees, a three-dimensional visualization technique to represent hierarchical information. in order to evaluate our proposal, we develop a prototype that generates standard 3D files so it can be easily integrated into existing tools. Finally, we present a roadmap for a future extension of our proposal with dynamic behaviour so large feature models handling might be improved.CICYT TIN2006-00472Junta de Andalucía TIC-253

Tool Supported Error Detection and Explanations on Feature Models

Automated analysis of feature models (FM) is a field of interest in recent years. Many operations over FMs have been proposed and developed, and many researchers and industrial companies have adopted FMs as a way to express variability. This last makes more necessary having support to detect, explain and fix errors on FMs. The notation of FMs makes very easy to express variability, but makes hard detecting errors and find their cause manually. and these errors may cause the model does not express the variability what we want of it. Therefore, we need support to detect errors and find their causes. The contribution of this paper is a method to detect errors in FMs, based on the concept of observation. We also present implementations of this approach and of an approach to explain errors, in FaMa Framework [1] tool. To detect FM errors, firstly we have to identify the different error types and what it means each of them. Void FM error means that the FM does not represent any product, dead feature error means that a feature of the FM does not appear in any product, false optional error means that an optional feature appears in every product that its parent feature also appears, and wrong cardinality error means that one or more values of a set relationship cardinality are not reachable. We can check for these errors in a intuitive way. For instance, to detect if a FM has dead features, we can calculate every product and check if each feature appears in, at least, one product. But further, we propose a method based on observations, it means, FM configurations associated with a specific element (feature or cardinality). Each type of error has its type of observation associated too. With an algorithm, we calculate the set of observations of a FM. Then, for each observation, we check if FM has at least one product. If not, we have found an error. For instance, dead feature observation sets its feature as selected. If the FM with a dead feature observation is not valid, it means the feature we are checking is dead. When we have found the errors, explanations tell us what is the cause of each error. An explanation is a set of relationships that originates one or more errors. Changing or removing these relationships we can fix a error. However, explanations by themselves do not provide information about how to change the relationship. For instance, if an explanation about a dead feature is a mandatory relationship, we can turn it into a optional relationship, but the explanation does not tell us directly. We have implemented observations and explanations approaches in FaMa Framework, a tool for the automated analysis of FMs. The observations approach implemented is the previously mentioned, while the explanations approach implemented is the one described by Trinidad et al. [3] [4]. With these approaches, we have detected errors in SPLOT FM repository [2], and we have obtained explanations for them also

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.CICYT TIN2006-0047

Feature Model to Orthogonal Variability Model Transformation Towards Interoperability Between Tools

Feature Model (FM) and Orthogonal Variability Model (OVM) are both modelling approaches employed to represent variability in software product line engineering. The former is the most popular and it is mainly applied to domain engineering. The later is a more recent approach mainly used to document variability in design and realisation artifacts. in the scenario of interest of our research, which focuses on Application Lifecycle Management environment, it would be useful rely on the FM to OVM transformation. To the best of our knowledge, in the literature, there is no proposal for such transformation. in this paper, we propose an algorithm to transform FM into OVM. This algorithm transforms the variable features of a FM into an OVM, thus providing an explicit view of variability of software product line. When working on these transformation, some issues came to light, such as how to preserve semantics. We discuss some of them and suggest a possible solution to transform FM into OVM by extending OVM

Automated Analysis of Stateful Feature Models

In CAiSE 2005, we interpreted the extraction of relevant information from extended feature models as an automated reasoning problem based on constraint programming. Such extraction is driven by a catalogue of basic and compound operations. Much has been done since, renaming the problem as the automated analysis of feature models, a widely accepted problem in the Software Product Line (SPL) community. In this chapter, we review this seminal contribution and its impact in the community, highlighting the key milestones up to a more complete problem formulation that we coin as the Automated Analysis of Stateful Feature Models (AASFM). Finally, we envision some breakthroughs and challenges in the AASFM.Ministerio de Ciencia e Innovación TIN2009- 07366Junta de Andalucía TIC-590

Abductive Reasoning and Automated Analysis of Feature Models: How are they connected?

In the automated analysis feature models (AAFM), many operations have been defined to extract relevant information to be used on decision making. Most of the proposals rely on logics to give solution to different operations. This extraction of knowledge using logics is known as deductive reasoning. One of the most useful operations are explanations that provide the reasons why some other operations find no solution. However, explanations does not use deductive but abductive reasoning, a kind of reasoning that allows to obtain conjectures why things happen. As a first contribution we differentiate between deductive and abductive reasoning and show how this difference affect to AAFM. Secondly, we broaden the concept of explanations relying on abductive reasoning, applying them even when we obtain a positive response from other operations. Lastly, we propose a catalog of operations that use abduction to provide useful information.Comisión Interministerial de Ciencia y Tecnología (CICYT) TIN2006-00472Junta de Andalucía TIC-253

Using Constraint Programming to Verify DOPLER Variability Models

Software product lines are typically developed using model-based approaches. Models are used to guide and automate key activities such as the derivation of products. The verification of product line models is thus essential to ensure the consistency of the derived products. While many authors have proposed approaches for verifying feature models there is so far no such approach for decision models. We discuss challenges of analyzing and verifying decision-oriented DOPLER variability models. The manual verification of these models is an error-prone, tedious, and sometimes infeasible task. We present a preliminary approach that converts DOPLER variability models into constraint programs to support their verification. We assess the feasibility of our approach by identifying defects in two existing variability models

Feature Model to Orthogonal Variability Model Transformations. A First Step

Feature Model (FM) and Orthogonal Variability Model (OVM) are both modelling approaches employed to represent variability in software product line engineering. The former is the most popular and it is mainly applied to domain engineering. The later is a more recent approach mainly used to document variability in design and realisation artifacts. in the scenario of interest of our research, which focuses on Application Lifecycle Management environment, it would be useful rely on the FM to OVM transformation. To the best of our knowledge, in the literature, there is no proposal for such transformation. in this paper, we propose an algorithm to transform FM into OVM. This algorithm transforms the variable features of a FM into an OVM, thus providing an explicit view of variability of software product line. When working on these transformation, some issues came to light, such as how to preserve semantics. We discuss some of them and suggest a possible solution to transform FM into OVM by extending OVM

Towards Anomaly Explanation in Feature Models

Feature models are a wide-spread approach to variability and commonality management in software product lines. Due to the increasing size and complexity of feature models, anomalies in terms of inconsistencies and redundancies can occur which lead to increased efforts related to feature model development and maintenance. In this paper we introduce knowledge representations which serve as a basis for the explanation of anomalies in feature models. On the basis of these representations we show how explanation algorithms can be applied. The results of a performance analysis show the applicability of these algorithms for anomaly detection in feature models. We conclude the paper with a discussion of future research issues

Automated metamorphic testing on the analyses of feature models

Copyright © 2010 Elsevier B.V. All rights reserved.Context: A feature model (FM) represents the valid combinations of features in a domain. The automated extraction of information from FMs is a complex task that involves numerous analysis operations, techniques and tools. Current testing methods in this context are manual and rely on the ability of the tester to decide whether the output of an analysis is correct. However, this is acknowledged to be time-consuming, error-prone and in most cases infeasible due to the combinatorial complexity of the analyses, this is known as the oracle problem.Objective: In this paper, we propose using metamorphic testing to automate the generation of test data for feature model analysis tools overcoming the oracle problem. An automated test data generator is presented and evaluated to show the feasibility of our approach.Method: We present a set of relations (so-called metamorphic relations) between input FMs and the set of products they represent. Based on these relations and given a FM and its known set of products, a set of neighbouring FMs together with their corresponding set of products are automatically generated and used for testing multiple analyses. Complex FMs representing millions of products can be efficiently created by applying this process iteratively.Results: Our evaluation results using mutation testing and real faults reveal that most faults can be automatically detected within a few seconds. Two defects were found in FaMa and another two in SPLOT, two real tools for the automated analysis of feature models. Also, we show how our generator outperforms a related manual suite for the automated analysis of feature models and how this suite can be used to guide the automated generation of test cases obtaining important gains in efficiency.Conclusion: Our results show that the application of metamorphic testing in the domain of automated analysis of feature models is efficient and effective in detecting most faults in a few seconds without the need for a human oracle.This work has been partially supported by the European Commission(FEDER)and Spanish Government under CICYT project SETI(TIN2009-07366)and the Andalusian Government project ISABEL(TIC-2533)