Use of genetic algorithm for software maintainability metrics' conditioning

The cost of software maintenance phase has always been a crucial issue for software project managers. With increasing complexity of modern software, there is an increased demand of measurement tools for software maintainability, so that it can be estimated in early phases of the project development and corrective measures can be initiated to make it more manageable and maintainable. The importance of measuring maintainability in the starting phases of software evolution has been widely acknowledged by researchers and software managers, but only few metrics have been proposed to measure it. Recently many researchers have proposed some integrated models for maintainability measurement, which need to be calibrated in spite of their reported validations, as no attention has been paid to evaluate and improve the stability of these methods. This paper proposes a methodology to improve the stability of a fuzzy logic based maintainability metrics system. Fuzzy system parameters are tuned using genetic algorithm with system condition number as objective function for optimization.