MEASURING THE COMPLEXITY OF NATURAL LANGUAGE REQUIREMENTS IN INDUSTRIAL CONTROL SYSTEMS

Requirements specification documents are one of the main sources of guidance in software engineering projects and they contribute to the definition of the final product and its attributes. They can often contain text, graphs, figures and diagrams. However, they are still mostly written in Natural Language (NL) in industry, which is also a convenient way of representing them. With the increase in the size of software projects in industrial systems, the requirements specification documents are often growing in size and complexity, that could result in requirements documents being not easy to analyze. There is a need to provide the stakeholders with a way of analyzing requirements in order to develop software projects more efficiently. In this thesis we investigate how the complexity of textual requirements can be measured in industrial systems. A set of requirements complexity measures was selected from the literature. These measures are adapted for application on real-world requirements specification documents. These measures are implemented in a tool called RCM and evaluated on requirements documentation provided by Bombardier Transportation AB. The statistical correlation between the selected measures was investigated based on a sample of data from the provided documentation. The statistical analysis has shown a significant correlation between a couple of selected measures. In addition, a focus group was performed with a goal of exploring the potential use of these metrics and the RCM tool in industrial systems as well as what different areas of potential improvement future research can investigate