73 research outputs found

    Automated metamorphic testing on the analyses of feature models

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    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)

    Visualizing Variability Models Using Hyperbolic Trees

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    Software Product Line Engineering (SPLE) has emerged in recent years as a viable way to maximize reuse when designing a family of related products. One of the main tasks conducted during the SPLE process is Variability Management (VM). VM is about identifying commonality among the different products being developed while capturing and cataloging variability. In real-life projects, VM models tend to encompass a very large number of variants reaching in many projects the order of thousands. Visualizing these models has been a major challenge for tool developers. In this work, we present our MUSA CASE tool which uses hyperbolic trees for representing VM models and supports gesture based interaction (using multi-touch interfaces). The tool has been successfully used to develop a large scale case study

    CASE Tool support for variability management in software product lines

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    Software product lines (SPL) aim at reducing time-to-market and increasing software quality through extensive, planned reuse of artifacts. An essential activity in SPL is variability management, i.e., defining and managing commonality and variability among member products. Due to the large scale and complexity of today’s software-intensive systems, variability management has become increasingly complex to conduct. Accordingly, tool support for variability management has been gathering increasing momentum over the last few years and can be considered a key success factor for developing and maintaining SPLs. While several studies have already been conducted on variability management, none of these analyzed the available tool support in detail. In this work, we report on a survey in which we analyzed 37 existing variability management tools identified using a systematic literature review to understand the tools’ characteristics, maturity, and the challenges in the field. We conclude that while most studies on variability management tools provide a good motivation and description of the research context and challenges, they often lack empirical data to support their claims and findings. It was also found that quality attributes important for the practical use of tools such as usability, integration, scalability, and performance were out of scope for most studies

    A Multiple Views Model for Variability Management in Software Product Lines

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    \With current trends towards moving variability from hardware to software, and given the increasing desire to postpone design decisions as much as is economically feasible, managing the variability from requirements elicitation to implementation is becoming a primary business requirement in the product line process. Nowadays, a medium size software system may encompass hundreds if not thousands of variability points introducing a new level of complexity that current techniques struggle to manage. In this paper, we present a new approach to variability management by introducing a multiple views model (4VM) where each view caters for specific set of concerns that relate to a particular group of stakeholders

    Variability-Modelling Practices in Industrial Software Product Lines: A Qualitative Study

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    Many organizations have transitioned from single-systems development to product-line development with the goal of increasing productivity and facilitating mass customization. Variability modelling is a key activity in software product-line development that deals with the explicit representation of variability using dedicated models. Variability models specify points of variability and their variants in a product line. Although many variability-modelling notations and tools have been designed by researchers and practitioners, very little is known about their usage, actual benefits or challenges. Existing studies mostly describe product-line practices in general, with little focus on variability modelling. We address this gap through a qualitative study on variability-modelling practices in medium- and large-scale companies using two empirical methods: surveys and interviews. We investigated companies' variability-modelling practices and experiences with the aim to gather information on 1) the methods and strategies used to create and manage variability models, 2) the tools and notations used for variability modelling, 3) the perceived values and challenges of variability modelling, and 4) the core characteristics of their variability models. Our results show that variability models are often created by re-engineering existing products into a product line. All of the interviewees and the majority of survey participants indicated that they represent variability using separate variability models rather than annotative approaches. We found that developers use variability models for many purposes, such as the visualization of variabilities, configuration of products, and scoping of products. Although we observed that high degree of heterogeneity exists in the variability-modelling notations and tools used by organizations, feature-based notations and tools are the most common. We saw huge differences in the sizes of variability models and their contents, which indicate that variability models can have different use cases depending on the organization. Most of our study participants reported complexity challenges that were related mainly to the visualization and evolution of variability models, and dependency management. In addition, reports from interviews suggest that product-line adoption and variability modelling have forced developers to think in terms of a product-line scenario rather than a product-based scenario

    A NUI Based Multiple Perspective Variability Modelling CASE Tool

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    With current trends towards moving variability from hardware to software, and given the increasing desire to postpone design decisions as much as is economically feasible, managing the variability from requirements elicitation to implementation is becoming a primary business requirement in the product line engineering process. One of the main challenges in variability management is the visualization and management of industry size variability models. In this demonstration, we introduce our CASE tool, MUSA. MUSA is designed around our work on multiple perspective variability modeling and is implemented using the state-of-the-art in NUI, multi-touch interfaces, giving it the power and flexibility to create and manage large-scale variability models with relative ease

    Software product line engineering: a practical experience

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    The lack of mature tool support is one of the main reasons that make the industry to be reluctant to adopt Software Product Line (SPL) approaches. A number of systematic literature reviews exist that identify the main characteristics offered by existing tools and the SPL phases in which they can be applied. However, these reviews do not really help to understand if those tools are offering what is really needed to apply SPLs to complex projects. These studies are mainly based on information extracted from the tool documentation or published papers. In this paper, we follow a different approach, in which we firstly identify those characteristics that are currently essential for the development of an SPL, and secondly analyze whether the tools provide or not support for those characteristics. We focus on those tools that satisfy certain selection criteria (e.g., they can be downloaded and are ready to be used). The paper presents a state of practice with the availability and usability of the existing tools for SPL, and defines different roadmaps that allow carrying out a complete SPL process with the existing tool support.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Magic P12-TIC1814, HADAS TIN2015-64841-R (cofinanciado con fondos FEDER), MEDEA RTI2018-099213-B-I00 (cofinanciado con fondos FEDER), TASOVA MCIU-AEI TIN2017-90644-RED
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