Variability Abstractions: Trading Precision for Speed in Family-Based Analyses (Extended Version)

Family-based (lifted) data-flow analysis for Software Product Lines (SPLs) is capable of analyzing all valid products (variants) without generating any of them explicitly. It takes as input only the common code base, which encodes all variants of a SPL, and produces analysis results corresponding to all variants. However, the computational cost of the lifted analysis still depends inherently on the number of variants (which is exponential in the number of features, in the worst case). For a large number of features, the lifted analysis may be too costly or even infeasible. In this paper, we introduce variability abstractions defined as Galois connections and use abstract interpretation as a formal method for the calculational-based derivation of approximate (abstracted) lifted analyses of SPL programs, which are sound by construction. Moreover, given an abstraction we define a syntactic transformation that translates any SPL program into an abstracted version of it, such that the analysis of the abstracted SPL coincides with the corresponding abstracted analysis of the original SPL. We implement the transformation in a tool, reconfigurator that works on Object-Oriented Java program families, and evaluate the practicality of this approach on three Java SPL benchmarks.Comment: 50 pages, 10 figure

Exact and Efficient Bayesian Inference for Privacy Risk Quantification (Extended Version)

Data analysis has high value both for commercial and research purposes. However, disclosing analysis results may pose severe privacy risk to individuals. Privug is a method to quantify privacy risks of data analytics programs by analyzing their source code. The method uses probability distributions to model attacker knowledge and Bayesian inference to update said knowledge based on observable outputs. Currently, Privug uses Markov Chain Monte Carlo (MCMC) to perform inference, which is a flexible but approximate solution. This paper presents an exact Bayesian inference engine based on multivariate Gaussian distributions to accurately and efficiently quantify privacy risks. The inference engine is implemented for a subset of Python programs that can be modeled as multivariate Gaussian models. We evaluate the method by analyzing privacy risks in programs to release public statistics. The evaluation shows that our method accurately and efficiently analyzes privacy risks, and outperforms existing methods. Furthermore, we demonstrate the use of our engine to analyze the effect of differential privacy in public statistics

Refinement for Transition Systems with Responses

Motivated by the response pattern for property specifications and applications within flexible workflow management systems, we report upon an initial study of modal and mixed transition systems in which the must transitions are interpreted as must eventually, and in which implementations can contain may behaviors that are resolved at run-time. We propose Transition Systems with Responses (TSRs) as a suitable model for this study. We prove that TSRs correspond to a restricted class of mixed transition systems, which we refer to as the action-deterministic mixed transition systems. We show that TSRs allow for a natural definition of deadlocked and accepting states. We then transfer the standard definition of refinement for mixed transition systems to TSRs and prove that refinement does not preserve deadlock freedom. This leads to the proposal of safe refinements, which are those that preserve deadlock freedom. We exemplify the use of TSRs and (safe) refinements on a small medication workflow.Comment: In Proceedings FIT 2012, arXiv:1207.348

Kurt Eisner: Ein unvollendetes Leben

Abstract. Understanding the challenges faced by real projects in evolving variability models, is a prerequisite for providing adequate support for such undertakings. We study the evolution of a model describing features and configurations in a large product line—the Linux kernel variability model. We analyze this evolution quantitatively and qualitatively. Our primary finding is that the Linux kernel model appears to evolve surprisingly smoothly. In the analyzed period, the number of features had doubled, and still the structural complexity of the model remained roughly the same. Furthermore, we provide an in-depth look at the effect of the kernel’s development methodologies on the evolution of its model. We also include evidence about edit operations applied in practice, evidence of challenges in maintaining large models, and a range of recommendations (and open problems) for builders of modeling tools.

A Few Considerations on Structural and Logical Composition in Specification Theories

Over the last 20 years a large number of automata-based specification theories have been proposed for modeling of discrete,real-time and probabilistic systems. We have observed a lot of shared algebraic structure between these formalisms. In this short abstract, we collect results of our work in progress on describing and systematizing the algebraic assumptions in specification theories.Comment: In Proceedings FIT 2010, arXiv:1101.426