Visualization and exploration of optimal variants in product line engineering

Abstract

The decision-making process in Product Line Engineering (PLE) is often concerned with variant qualities such as cost, battery life, or security. Pareto-optimal variants, with respect to a set of objectives such as minimizing a variant's cost while maximizing battery life and security, are variants in which no single quality can be improved without sacrificing other qualities. We propose a novel method and a tool for visualization and exploration of a multi-dimensional space of optimal variants (i.e., a Pareto front). The visualization method is an integrated, interactive, and synchronized set of complementary views onto a Pareto front specifically designed to support PLE scenarios, including: understanding differences among variants and their positioning with respect to quality dimensions; solving trade-offs; selecting the most desirable variants; and understanding the impact of changes during product line evolution on a variant's qualities. We present an initial experimental evaluation showing that the visualization method is a good basis for supporting these PLE scenarios.

Performance-influence models for highly configurable systems

“…This observation suggests that missing a single term likely affects only a small fraction of the configuration space, such that the overall prediction error remains small. These results suggest that we indeed learn the actual existing influences in most cases, which is useful for performance debugging and interactive configuration tools [19].…”

“…Beside various facets of performance, performance-influence models may be beneficial to reason about other non-functional properties and quality attributes, most notably, energy consumption. Moreover, we can supply the models we learned to other performance-modeling and optimization tools, such as Clafer [19] and EPOAL [9].…”

Variability-aware performance prediction: A statistical learning approach

“…Our approach has the potential of wide application to help users make trade-offs between feature selections and performance and to guide the configuration process [15]. In future work, we aim at performing systematic parameter tuning for CART and trying other regression techniques (e.g., Support Vector Machines [4]).…”

Applying the product lines paradigm to the quantitative analysis of collective adaptive systems

“…It has successfully been used to model and optimize product lines [1,16]. In [15], it has been used for architecturally modeling a realistic automotive scenario.…”

“…The ClaferMOO extension of Clafer was specifically introduced to support attributed feature models as well as the resulting complex multi-objective optimizations [1,15,16]. A multi-objective optimization problem has a set of solutions, known as the Pareto front, that represents the trade-offs between two or more conflicting objectives.…”