Identifying and visualizing variability in object-oriented variability-rich systems

International audienceIn many variability-intensive systems, variability is implemented in code units provided by a host language, such as classes or functions, which do not align well with the domain features. Annotating or creating an orthogonal decomposition of code in terms of features implies extra effort, as well as massive and cumbersome refactoring activities. In this paper, we introduce an approach for identifying and visualizing the variability implementation places within the main decomposition structure of object-oriented code assets in a single variability-rich system. First, we propose to use symmetry, as a common property of some main implementation techniques, such as inheritance or overloading, to identify uniformly these places. We study symmetry in different constructs (e.g., classes), techniques (e.g., subtyping, overloading) and design patterns (e.g., strategy, factory), and we also show how we can use such symmetries to find variation points with variants. We then report on the implementation and application of a toolchain, symfinder, which automatically identifies and visualizes places with symmetry. The publicly available application to several large open-source systems shows that symfinder can help in characterizing code bases that are variability-rich or not, as well as in discerning zones of interest w.r.t. variability

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

Negative Results in Computer Vision: A Perspective

A negative result is when the outcome of an experiment or a model is not what is expected or when a hypothesis does not hold. Despite being often overlooked in the scientific community, negative results are results and they carry value. While this topic has been extensively discussed in other fields such as social sciences and biosciences, less attention has been paid to it in the computer vision community. The unique characteristics of computer vision, particularly its experimental aspect, call for a special treatment of this matter. In this paper, I will address what makes negative results important, how they should be disseminated and incentivized, and what lessons can be learned from cognitive vision research in this regard. Further, I will discuss issues such as computer vision and human vision interaction, experimental design and statistical hypothesis testing, explanatory versus predictive modeling, performance evaluation, model comparison, as well as computer vision research culture

The state of adoption and the challenges of systematic variability management in industry

Handling large-scale software variability is still a challenge for many organizations. After decades of research on variability management concepts, many industrial organizations have introduced techniques known from research, but still lament that pure textbook approaches are not applicable or efficient. For instance, software product line engineering—an approach to systematically develop portfolios of products—is difficult to adopt given the high upfront investments; and even when adopted, organizations are challenged by evolving their complex product lines. Consequently, the research community now mainly focuses on re-engineering and evolution techniques for product lines; yet, understanding the current state of adoption and the industrial challenges for organizations is necessary to conceive effective techniques. In this multiple-case study, we analyze the current adoption of variability management techniques in twelve medium- to large-scale industrial cases in domains such as automotive, aerospace or railway systems. We identify the current state of variability management, emphasizing the techniques and concepts they adopted. We elicit the needs and challenges expressed for these cases, triangulated with results from a literature review. We believe our results help to understand the current state of adoption and shed light on gaps to address in industrial practice.This work is supported by Vinnova Sweden, Fond Unique Interminist´eriel (FUI) France, and the Swedish Research Council. Open access funding provided by University of Gothenbur

A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes

International audienceWe present the generalized space-time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space-time cube operations and explain how these operations can be combined and parameterized. The generalized space-time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo-spatial, videos, networks, multivariate data). The proper choice of space-time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space-time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bach et al.'s (2014) work

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

Visualizing IS Course Objectives and Marketable Skills

Course and learning objectives are important tools for setting goals, navigating the course, and measuring performance. Unfortunately, when multiple interrelated objectives are presented as a list of statements, students perceive them as having little utility and tend to misunderstand or ignore them. To increase students’ attention to course objectives, to help them understand the arc, structure, and valuable outcomes of the course, and to engage in active learning, we propose an approach to presenting course objectives in a visual form. The evidence suggests that visualized objectives increased students’ interest in understanding them and provided aids to instructors to better explain how various components of the course fit together and translate into marketable skills. We recommend practical steps for visualizing objectives in any course and present examples of visualizations in two IS courses – “Enterprise Architecture” and “Systems Analysis and Design.