Procedure compilation in the refinement calculus

High-level language program compilation strategies can be proven correct by modelling the process as a series of refinement steps from source code to a machine-level description. We show how this can be done for programs containing recursively-defined procedures in the well-established predicate transformer semantics for refinement. To do so the formalism is extended with an abstraction of the way stack frames are created at run time for procedure parameters and variables.

Managing change and time in dynamic workflow processes

Business environments have become exceedingly dynamic and competitive in recent times. This dynamism is manifested in the form of changing process requirements and time constraints. Workflow technology is currently one of the most promising fields of research in business process automation. However, workflow systems to date do not provide the flexibility necessary to support the dynamic nature of business processes. In this paper we primarily discuss the issues and challenges related to managing change and time in workflows representing dynamic business processes. We also present an analysis of workflow modifications and provide feasibility considerations for the automation of this process.

Using measurement data in a TSPSM project

One of the challenges for software engineering is collecting meaningful data from industrial projects. Software process improvement depends on measurement to provide baseline status and confirming evidence of the effect of process changes. Without data, any conclusions rely on intuition and guessing. The Team Software ProcessSM (TSPSM) provides a powerful framework for data collection and analysis, in addition to its primary goal as a basis for highly effective software development. In this paper, we describe the experiences of, and benefits realized by, a team using the TSP for the first time. By reviewing how this particular team collected and used data, we show features of the TSP that make it a powerful foundation for software process improvement.

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in border to have a clear and detailed picture of their performance. Unfortunately, the total number of experiments required may be very large, which often makes such research work computationally prohibitive. In this paper, the application of a statistical method called racing is proposed as a general-purpose tool to reduce the computational requirements of large-scale experimental studies in evolutionary algorithms. Experimental results are presented that show that racing typically requires only a small fraction of the cost of an exhaustive experimental study.