Model-driven engineering (MDE) often features quality assurance (QA) techniques to help developers creating software that meets reliability, efficiency, and safety requirements. In this paper, we consider the question of how quality-aware MDE should support data-intensive software systems. This is a difficult challenge, since existing models and QA techniques largely ignore properties of data such as volumes, velocities, or data location. Furthermore, QA requires the ability to characterize the behavior of technologies such as Hadoop/MapReduce, NoSQL, and stream-based processing, which are poorly understood from a modeling standpoint. To foster a community response to these challenges, we present the research agenda of DICE, a quality-aware MDE methodology for data-intensive cloud applications. DICE aims at developing a quality engineering tool chain offering simulation, verification, and architectural optimization for Big Data applications. We overview some key challenges involved in developing these tools and the underpinning models.

International audienceThe equivalence checking problem consists in verifying that a system (e.g., a protocol) matches its abstract specification (e.g., a service) by comparing their Labeled Transition Systems (Ltss) modulo a given equivalence relation. Two approaches are traditionally used to perform equivalence checking: global verification requires to construct the two Ltss before comparison, whereas local (or on-the-fly) verification allows to explore them incrementally during comparison. The latter approach is able to detect errors even in prohibitively large systems, and therefore reveals more effective in combating state explosion

Abstract-The well-known problem of state space explosion in model checking is even more critical when applying this technique to programming languages, mainly due to the presence of complex data structures. One recent and promising approach to deal with this problem is the construction of an abstract and correct representation of the global program state allowing to match visited states during program model exploration. In particular, one powerful method to implement abstract matching is to fill the state vector with a minimal amount of relevant variables for each program point. In this paper, we combine the on-the-fly model checking approach (incremental construction of the program state space) and the static analysis method called influence analysis (extraction of significant variables for each program point) in order to automatically construct an abstract matching function. Firstly, we describe the problem as an alternation-free value-based µ-calculus formula, whose validity can be checked on the program model expressed as a labeled transition system (LTS). Secondly, we translate the analysis into the local resolution of a parameterised boolean equation system (PBES), whose representation enables a more efficient construction of the resulting abstract matching function. Finally, we show how our proposal has been elegantly integrated into CADP, a generic framework for both the design and analysis of distributed systems and the development of verification tools.

This paper describes a set of analysis components that open the way to perform performance and dependability analysis with the Cadp toolbox, originally designed for verifying the functional correctness of Lotos specifications. Three new tools (named Bcg Steady, Bcg Transient and Determinator) have been added to the toolbox. The approach taken fits well within the existing architecture of Cadp which doesn't need to be altered to enable performance evaluation.