Fixing state change inconsistency with self regulating particle swarm optimization

Software has made a profound influence in all walks of life. Developing quality software is a major challenge, and the consistency and completeness of the design has a prime role in the development of quality software. Many a times, the process of consistency checking in industries is manual. Artificial intelligence techniques can replace many of these manual efforts to make the development of software easier and cost-effective. Software developers use state diagrams to represent the dynamic behavior in the design stage. We propose a novel application of self regulating particle swarm optimization (SRPSO) algorithm to ensure consistency of state diagrams during the design phase of software development. Inconsistency management is modeled as an optimization problem. In this work, we detect two types of state change inconsistency, incompatible behavior inconsistency and disconnected model inconsistency. A fitness function is defined to detect inconsistency. We make use of the SRPSO algorithm to resolve inconsistency. Detecting inconsistencies in the early stages of software development enables phase containment of errors and prevents errors from being propagated to the code. The proposed approach generates consistent and complete state diagrams leading to accurate code generation, meeting time deadlines, reducing cost of production and easy system maintenance.