Performance prediction for industrial software with the APPEAR method

The Analysis and Prediction of Performance for Evolving Architectures (APPEAR) method aims at the performance estimation of newly developed or adapted parts of software product families during the architecting phase. Early performance prediction allows checking the feasibility of systems before their implementation and thus saves money and effort from developing potentially infeasible products. In contrast to all the existing methods, it combines both structural and statistical techniques. It allows choosing which part of the application is structurally modeled, and which part is statistically approximated. The statistical approach is employed to model those parts of a system that remain unchanged for a long time during the evolution. The analytical approach is used to model the parts of the system that evolve rapidly and that are thus not yet implemented. Also here, statistical modeling helps to abstract from internal details of components and thus to reduce the modeling complexity. Often, a simulation model can be built that provides fast feedback on the changes of relevant parts. The method was checked using case studies in the Consumer Electronics and the Medical Imaging System domains. The initial results are encouraging for the case of single components. The APPEAR method is currently being extended to address performance prediction for component compositions

Quantitative architecture usability assessment with scenarios

In this paper, we propose a quantitative scenario-based method for dealing with the quality attributes for future software systems before any architecture implementation effort is actually done. The exploration of the possible design variants for a future system is done using so-called user scenarios. The method supports the software architects in developing future systems that meet their specific quality requirements. The method was validated in an industrial case study focusing on the usability aspects

Excess enthalpies from displacement calorimetry excess enthalpies for 1,1,1- trichloroethane+carbon tetrachloride and 2-chloro-2-methylpropane+carbon tetrachloride at 298.15 K

Excess enthalpies HE are reported for the 1,1,1-trichloroethane+carbon tetrachloride and 2-chloro-2-methylpropane+carbon tetrachloride systems. The results are fitted to the formula HE = x(1−x)Σiai(1−2x)i

Excess enthalpies from displacement calorimetry excess enthalpies for 1,1,1- trichloroethane+carbon tetrachloride and 2-chloro-2-methylpropane+carbon tetrachloride at 298.15 K

Excess enthalpies HE are reported for the 1,1,1-trichloroethane+carbon tetrachloride and 2-chloro-2-methylpropane+carbon tetrachloride systems. The results are fitted to the formula HE = x(1−x)Σiai(1−2x)i

A scenario-driven approach for value, risk and cost analysis in system architecting for innovation

We present a quantitative method for scenario-driven value, risk, and cost analysis when proposing new system architectures for innovation projects. The method helps to articulate the relative benefits and/or disadvantages of the proposed set of scenarios in the early architecting phases of a new system. It provides the arguments on which to base an informed decision to select the final architectural scenarios for further consideration in the design. In this paper we present a case study in which we applied the proposed method