Towards Visualization of Feature Interactions in Software Product Lines

Illescas, Sheny; Lopez-Herrejon, Roberto E.; Egyed, Alexander

doi:10.1109/vissoft.2016.16

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…Here, our study found that colors are used to describe what software artifacts or their pieces belong to particular features. For instance, Heidenreich et al use colors to annotate UML‐based models, Kästner et al follow the same idea but applied to source code, while Illescas et al use colors to distinguish features and their interactions …”

Section: Resultsmentioning

confidence: 99%

“…For example, understanding how features and their relations are actually realized at source code can help, for instance, during testing and maintenance tasks. Existing feature to code traceability tools could be used; for an early example, see Illescas et al In addition, another avenue for research is the use of multivariate data visualization techniques (eg, 2 books()), which could be helpful to understand relations among artifacts, and specially for handling complex relations among artifacts, for example, by reducing their dimensionality. It should also be mentioned that all the visualizations use the same modalities of interaction, that is, input from keyboard and mouse.…”

Section: Research Avenuesmentioning

confidence: 99%

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A systematic mapping study of information visualization for software product line engineering

Lopez-Herrejon

Illescas

Egyed

2017

J Software Evolu Process

Self Cite

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Software product lines (SPLs) are families of related systems whose members are distinguished by the set of features they provide. Over 2 decades of research and practice can attest to the substantial benefits of applying SPL practices such as better customization, improved software reuse, and faster time to market. Software product line engineering (SPLE) refers to the paradigm of developing SPLs. Typical SPLE efforts involve a large number of features that are combined to form also large numbers of products, implemented using multiple and different types of software artifacts. Because of the sheer amount of information and its complexity, visualization techniques have been used for different SPLE activities. In this paper, we present an extended systematic mapping study on this subject. Our research questions aim to gather information regarding the techniques that have been applied, at what SPLE activities, how they were implemented, the publication fora used, the methods of empirical evaluation, and the provenance of the evaluation examples. Our driving goal is to identify common trends, gaps, and opportunities for further research and application.

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Section: Resultsmentioning

confidence: 99%

Section: Research Avenuesmentioning

confidence: 99%

A systematic mapping study of information visualization for software product line engineering

Lopez-Herrejon

Illescas

Egyed

2017

J Software Evolu Process

Self Cite

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“…The visualization and SPL research have crossed paths on several occasions, but from a different perspective than the one presented in this article. Studies focus on applying visualization techniques in the context of SPLs [49] mainly for the visualization of feature models [48], but also to visualize variability and configurations [4,50], feature interactions [27], and constraints [39]. Techniques include the use of colors to visualize variabilities in source code [18,30], polymetric views [36] in the form of a variability blueprint for the decomposition of complex feature models [62], feature relations graphs (Frogs) for feature constraints [39], cone trees to draw feature models in 3D [60], or the use of statistics to visualize largescale feature models [24], among others.…”

Section: Related Workmentioning

confidence: 99%

Variability in data visualization

Horcas

Galindo

Benavides

2022

Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A

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Data visualization aims to effectively communicate quantitative information by understanding which techniques and displays work better for different circumstances and why. There are a variety of software solutions capable of generating a multitude of different visualizations of the same dataset. However, data visualization exposes a large space of visual configurations depending on the type of data to be visualized, the different displays (e.g., scatter plots, line graphs, pie charts), the visual components to encode the data (e.g., lines, dots, bars), or the specific visual attributes of those components (e.g., color, shape, size, length). Researchers and developers are not usually aware about best practices in data visualization, and they are required to learn about both the design practices that make communication effective and the low level details of the specific software tool used to generate the visualization. This paper proposes a software product line approach to model and materialize the variability of the visualization design process, guided by feature models. We encode the visualization knowledge regarding the best design practices, resolve the variability following a step-wise configuration approach, and then evaluate our proposal for a specific software visualization tool. Our solution helps researchers and developers communicate their quantitative results effectively by assisting them in the selection and generation of the visualizations that work best for each case. We open a new window of research where data visualization and variability meet each other. CCS CONCEPTS• Software and its engineering → Software product lines; •␣ Human-centered computing → Information visualization; Graph drawings.

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“…e Feature Relation Graph presents possible feature combinations depending on a selected feature [16]. Illescas et al as well as Urli et al show di erent visualization models for feature combinations but without a connection to the source code [9,24]. Many works are based on the same extraction tools such as Feature-CoPP [12], SuperC [8], TypeChef [10], and Yacfe [21].…”

Section: Related Workmentioning

confidence: 99%

Understanding Conditional Compilation through Integrated Representation of Variability and Source Code

Baum

Sixtus

Vogelsberg

et al. 2019

Proceedings of the 23rd International Systems and Software Product Line Conference - Volume B

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e C preprocessor (CPP) is a standard tool for introducing variability into source programs and is o en applied either implicitly or explicitly for implementing a So ware Product Line (SPL). Despite its practical relevance, CPP has many drawbacks. Because of that it is very di cult to understand the variability implemented using CPP. To facilitate this task we provide an innovative analytics tool which bridges the gap between feature models as more abstract representations of variability and its concrete implementation with the means of CPP. It allows to interactively explore the entities of a source program with respect to the variability realized by conditional compilation. us, it simpli es tracing and understanding the e ect of enabling or disabling feature ags.

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Towards Visualization of Feature Interactions in Software Product Lines

Cited by 6 publications

References 23 publications

A systematic mapping study of information visualization for software product line engineering

A systematic mapping study of information visualization for software product line engineering

Variability in data visualization

Understanding Conditional Compilation through Integrated Representation of Variability and Source Code

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