Concerns and their separation in feature diagram languages: An informal survey

Feature diagrams describe valid configurations of features in a product. As feature diagrams play a central role in software product line development, several concerns are reflected in them, contributing to their increasing size and complexity. The way that those concerns are recognised and separated in feature diagrams affects our ability to manage them effectively. However, the nature of important concerns in software product line development, and the extent to which the separation of concerns is addressed by current research and practice is not clear. In this paper, we report on our initial survey of important concerns considered by feature diagram languages and guidelines for addressing those concerns

On the structure of problem variability: From feature diagrams to problem frames

Requirements for product families are expressed in terms of commonality and variability. This distinction allows early identification of an appropriate software architecture and opportunities for software reuse. Feature diagrams provide intuitive notations and techniques for representing requirements in product line development. In this paper, we observe that feature diagrams tend to obfuscate three important descriptions: requirements, domain properties and specifications. As a result, feature diagrams do not adequately capture the problem structures that underlie variability, and inform the solution structures of their complexity. With its emphasis on separation of the three descriptions, the problem frames approach provides a conceptual framework for a more detailed analysis of variability and its structure. With illustrations from an example, we demonstrate how problem frames analysis of variability can augment feature diagrams

Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning

Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming under increasing scrutiny due to this widespread use and its theoretical considerations. Individuals, as well as organisations, notice, test, and criticize unfair results to hold model designers and deployers accountable. We offer a framework that assists these groups in mitigating unfair representations stemming from the training datasets. Our framework relies on two inter-operating adversaries to improve fairness. First, a model is trained with the goal of preventing the guessing of protected attributes' values while limiting utility losses. This first step optimizes the model's parameters for fairness. Second, the framework leverages evasion attacks from adversarial machine learning to generate new examples that will be misclassified. These new examples are then used to retrain and improve the model in the first step. These two steps are iteratively applied until a significant improvement in fairness is obtained. We evaluated our framework on well-studied datasets in the fairness literature -- including COMPAS -- where it can surpass other approaches concerning demographic parity, equality of opportunity and also the model's utility. We also illustrate our findings on the subtle difficulties when mitigating unfairness and highlight how our framework can assist model designers.Comment: 15 pages, 3 figures, 1 tabl

Towards Statistical Prioritization for Software Product Lines Testing

Software Product Lines (SPL) are inherently difficult to test due to the combinatorial explosion of the number of products to consider. To reduce the number of products to test, sampling techniques such as combinatorial interaction testing have been proposed. They usually start from a feature model and apply a coverage criterion (e.g. pairwise feature interaction or dissimilarity) to generate tractable, fault-finding, lists of configurations to be tested. Prioritization can also be used to sort/generate such lists, optimizing coverage criteria or weights assigned to features. However, current sampling/prioritization techniques barely take product behavior into account. We explore how ideas of statistical testing, based on a usage model (a Markov chain), can be used to extract configurations of interest according to the likelihood of their executions. These executions are gathered in featured transition systems, compact representation of SPL behavior. We discuss possible scenarios and give a prioritization procedure illustrated on an example.Comment: Extended version published at VaMoS '14 (http://dx.doi.org/10.1145/2556624.2556635

A Text-based Approach to Feature Modelling: Syntax and Semantics of TVL

International audienceIn the scientific community, feature models are the de-facto standard for representing variability in software product line engineering. This is different from industrial settings where they appear to be used much less frequently. We and other authors found that in a number of cases, they lack concision, naturalness and expressiveness. This is confirmed by industrial experience. When modelling variability, an efficient tool for making models intuitive and concise are feature attributes. Yet, the semantics of feature models with attributes is not well understood and most existing notations do not support them at all. Furthermore, the graphical nature of feature models' syntax also appears to be a barrier to industrial adoption, both psychological and rational. Existing tool support for graphical feature models is lacking or inadequate, and inferior in many regards to tool support for text-based formats. To overcome these shortcomings, we designed TVL, a text-based feature modelling language. In terms of expressiveness, TVL subsumes most existing dialects. The main goal of designing TVL was to provide engineers with a human-readable language with a rich syntax to make modelling easy and models natural, but also with a formal semantics to avoid ambiguity and allow powerful automation

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