Carlos A P Campani

This paper presents a proposal for the application of Kolmogorov complexity to the characterization of systems and processes, and the evaluation of computational models. The methodology developed represents a theoretical tool to solve problems from systems science. Two applications of the methodology are presented in order to illustrate the proposal, both of which were developed by the authors. The first one is related to the software development process, the second to computer animation models. In the end a third application of the method is briefly introduced, with the intention of characterizing dynamic systems of chaotic behavior, which clearly demonstrates the potentials of the methodology.

This paper presents a proposal for the application of Kolmogorov complexity to the characterization of systems
and processes, and the evaluation of computational models.
The methodology developed represents a theoretical tool to
solve problems from systems science. Two applications of the
methodology are presented in order to illustrate the proposal,
both of which were developed by the authors. The first one
is related to the software development process, the second to
computer animation models. In the end a third application of the method is briefly introduced, with the intention of characterizing dynamic systems of chaotic behavior, which clearly demonstrates the potentials of the methodology.

This paper introduces a new methodology, based on Kolmogorov complexity, to compare and evaluate computer animation models, regarding data compression. It was originally proposed to be applyed to characterize the software development process. The methodology proposed
here may be used on a more general context, as a tool to solve problems in systems science. Systems science has a vague definition. It is related to the study of general principles governing systems of a large range of types. In
our method, we define an animation model as a machine and use machine simulation to compare diferent types of models, setting out an animation model hierarchy. A definition of
data compression is provided to support our definition of "better animation". The definition includes lossy data compression and may be applyed to MPEG streams. The methodology is theoretical and related to classic computing
theory.

Our main aim is to propose a new characterization for the software development process. We suggest that software development methodology has some limits. These limits are a clue that software development process is more subjective and empirical than objective and formal. We use Kolmogorov Complexity to develop the formal argument and to outline the informal conlusions. Kolmogorov Complexity is based on the size in bits of the smallest effective description of an object and is a suitable quantitative measure of the object's information content. We try to show that notion of complexity is a suitable measure and a tool for the characterization of the software development process. Following the paper conclusions, the limits of formal methods typifies the software development process as experimental and heuristical based, like, for example, the scientific development in physics and chemistry. Moreover, by our approach, we argue that software development is, in some sense, formally unpredictable. These conclusions suggest that software engineering is a scientific field not totally characterized by the typical work of engineering, but also by the experimental sciences methodology.