Probabilistic model checking for energy analysis in software product lines

Abstract

In a software product line (SPL), a collection of software products is defined by their commonalities in terms of features rather than explicitly specifying all products one-by-one. Several verification techniques were adapted to establish temporal properties of SPLs. Symbolic and family-based model checking have been proven to be successful for tackling the combinatorial blow-up arising when reasoning about several feature combinations. However, most formal verification approaches for SPLs presented in the literature focus on the static SPLs, where the features of a product are fixed and cannot be changed during runtime. This is in contrast to dynamic SPLs, allowing to adapt feature combinations of a product dynamically after deployment.The main contribution of the paper is a compositional modeling framework for dynamic SPLs, which supports probabilistic and nondeterministic choices and allows for quantitative analysis. We specify the feature changes during runtime within an automatabased coordination component, enabling to reason over strategies how to trigger dynamic feature changes for optimizing various quantitative objectives, e.g., energy or monetary costs and reliability. For our framework there is a natural and conceptually simple translation into the input language of the prominent probabilistic model checker PRISM. This facilitates the application of PRISM's powerful symbolic engine to the operational behavior of dynamic SPLs and their family-based analysis against various quantitative queries. We demonstrate feasibility of our approach by a case study issuing an energy-aware bonding network device.

Conditional transition systems with upgrades

“…however, given the nature of our work, below we rather concentrate only on the formal ones [10,11,13,27].…”

Probabilistic Model Checking and Non-standard Multi-objective Reasoning

“…Family-based analysis approaches [39], where all possible software products are checked at once instead of checking each feature combination one-by-one, turned out to be very successful [16]. Recently, we proposed a compositional framework for probabilistic SPLs which supports dynamic feature changes and has an MDP-based semantics, allowing for quantitative analysis using standard PMC-methods [22]. We demonstrated feasibility of our approach by analyzing an extended version of the energy-aware bonding network device eBond [26].…”

“…It is obvious that the approaches of computing quantiles, conditionals and ratios can be further combined, which enables to provide even more insights on feature-oriented quantitative properties of eBond+ than already demonstrated in [22] within single objectives.…”

“…They can be useful to predict the performance of management algorithms on future hardware or low-level protocols that have not been implemented yet, making measurements are impossible. We successfully applied PMC, e.g., for the analysis of a spinlock protocol [5], a lock-free synchronization protocol for read-write problems [6], a bonding network device [22] and an energy-aware job scheduling scenario [4].…”

“…We illustrate how the exemplified tasks stated above can be used in the field of software product line verification. Based on our recent work [22], we detail its application to the energy-aware server system eBond+.…”