Recent new technologies and paradigm shifts in the IT business make the role of performance engineers significantly more challenging than any other time in the past. Flash technology, virtualization, and Cloud computing provide new options for performance optimization; however, materializing the potential of these technologies in a predictable and cost effective manner is a challenge. New performance management software and planning tools that are based on scientific research and analysis are required to meet the new expectations that users have.

We describe the semantics of statecharts as implemented in the STATEMATE system. This was the first executable semantics defined for the language and has been in use for almost a decade. In terms of the controversy around whether changes made in a given step should take effect in the current step or in the next one, this semantics adopts the latter approach.

STATEMATE is a set of tools, with a heavy graphical orientation, intended for the specification, analysis, design, and documentation of large and complex reactive systems. It enables a user to prepare, analyze, and debug diagrammatic, yet precise, descriptions of the system under development from three interrelated points of view, capturing structure, functionality, and behavior. These views are represented by three graphical languages, the most intricate of which is the language of statecharts, used to depict reactive behavior over time. In addition to the use of statecharts, the main novelty of STATEMATE is in the fact that it understands the entire descriptions perfectly, to the point of being able to analyze them for crucial dynamic properties, to carry out rigorous executions and simulations of the described system, and to create running code automatically. These features are invaluable when it comes to the quality and reliability of the final outcome.>

A new dynamizing technique is introduced wherebynpoint Voronoi diagrams (both closest and farthest point) can be updated inO(n)time per insertion or deletion, in the worst case. General properties of these dynamic Voronoi diagrams are explored including a storage/ deletion-time trade-off. In addition, their application to such problems as nearest neighbor search and the 2-minimum spanning circle problem is discussed.

An O(EVlog2V) algorithm for finding the maximal flow in networks is described. It is asymptotically better than the other known algorithms if E = O(V2-ε) for some ε>0. The analysis of the running time exploits the discovery of a phenomenon similar to (but more general than) path compression, although the union find algorithm is not used. The time bound is shown to be tight in terms of V and E by exhibiting a family of networks that require Ω(EVlog2V) time.++