Parallel fuzzy cognitive maps as a tool for modeling software development projects

Fuzzy cognitive maps (FCM) are useful tool for simulating and analyzing dynamic systems. The FCMs have a very simple structure, and thus are very easy to comprehend and use. Despite of the simplicity, they have been successfully adopted in many different areas, such as electrical engineering, medicine political science, international relations, military science, history: supervisory systems, etc. Software development is a complex process, and there are many factors that influence its progress. To effectively handle larger development processes, they are usually divided into subtasks, which are assigned to different teams of workers, and often are performed in parallel. However, some constraints that impose particular sequence of realization of these subtasks, i.e. some tasks cannot be started before completing others, usually exist. Proper division of a project into subtasks and establishing relations between them are essential to correctly manage software projects. Neglecting these constraints often leads to problems that, in consequence, cause misestimating the overall time and budget. This paper introduces a new architecture of FCM, which combines a number of simple FCM models that work simultaneously into a novel parallel FCMs model. It uses a special purpose coordinator module to synchronize simulation of each FCM model. This approach extends application of FCMs to complex systems, which contain multiple subtasks that run in parallel, and thus must be simulated with multiple FCM models. In addition, application of parallel FCMs to analyze and design software development processes is presented. FCM models are focused on simulating and analyzing factors, such as progress and communication, and their relationships, which are based on theoretical research studies and practical implementations. The parallel FCM model is used to simulate complex projects where multiple tasks exist. The paper is based on our previous work where FCM models, which describe relationships between the above factors for individual development tasks, were developed. The newly proposed architecture allows for efficient analysis of dependences between tasks performed in parallel.