Computational Intelligence in Software Cost Estimation: Evolving Conditional Sets of Effort Value Ranges

In the area of software engineering a critical task is to accurately estimate the overall project costs for the completion of a new software project and efficiently allocate the resources throughout the project schedule. The numerous software cost estimation approaches proposed are closely related to cost modeling and recognize the increasing need for successful project management, planning and accurate cost prediction. Cost estimators are continually faced with problems stemming from the dynamic nature of the project development process itself. Software development is considered an intractable procedure and inevitably depends highly on several complex factors (e.g., specification of the system, technology shifting, communication, etc.). Normally, software cost estimates increase proportionally to development complexity rising, whereas it is especially hard to predict and manage the actual related costs. Even for well-structured and planned approaches to software development, cost estimates are still difficult to make and will probably concern project managers long before the problem is adequately solved. During a system’s life-cycle, one of the most important tasks is to effectively describe the necessary development activities and estimate the corresponding costs. This estimation, once successful, allows software engineers to optimize the development process, improve administration and control over the project resources, reduce the risks caused by contingencies and minimize project failures (Lederer & Prasad, 1992). Subsequently, a commonly investigated approach is to accurately estimate some of the fundamental characteristics related to cost, such as effort and schedule, and identify their interassociations. Software cost estimation is affected by multiple parameters related to technologies, scheduling, manager and team member skills and experiences, mentality and culture, team cohesion, productivity, project size, complexity, reliability, quality and many more. These parameters drive software development costs either positively or negatively and are considerably very hard to measure and manage, especially at an early project development phase. Hence, software cost estimation involves the overall assessment of these parameters, even though for the majority of the projects, the most dominant and popular metric is the effort cost, typically measured in person-months. Recent attempts have investigated the potential of employing Artificial Intelligence-oriented methods to forecast software development effort, usually utilising publicly available