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.

软件在各行各业都产生了深远的影响。开发高质量软件是一项重大挑战，设计的一致性和完整性在开发高质量软件中起着主要作用。很多时候，行业中的一致性检查过程是手动的。人工智能技术可以代替许多人工工作，从而使软件开发更加轻松且具有成本效益。软件开发人员使用状态图来表示设计阶段的动态行为。我们提出了一种新的自调节粒子群优化（SRPSO）算法应用程序，以确保在软件开发的设计阶段状态图的一致性。不一致管理被建模为一个优化问题。在这项工作中，我们检测到两种类型的状态变化不一致，不兼容的行为不一致和断开的模型不一致。定义了适应度函数以检测不一致。我们利用SRPSO算法来解决不一致问题。在软件开发的早期阶段检测不一致性可以在阶段中包含错误，并防止错误传播到代码中。所提出的方法将生成一致且完整的状态图，从而导致准确的代码生成，满足时间期限，降低生产成本并简化系统维护。在软件开发的早期阶段检测不一致性可以在阶段中包含错误，并防止错误传播到代码中。所提出的方法将生成一致且完整的状态图，从而导致准确的代码生成，满足时间期限，降低生产成本并简化系统维护。在软件开发的早期阶段检测不一致性可以在阶段中包含错误，并防止错误传播到代码中。所提出的方法将生成一致且完整的状态图，从而导致准确的代码生成，满足时间期限，降低生产成本并简化系统维护。