A New Methodology for Quantifying the Impact of Non-Functional Requirements on Software Effort Estimation

The effort estimation techniques used in the software industry often tend to ignore the impact of Non-functional Requirements (NFR) on effort and reuse standard effort estimation models without local calibration. Moreover, the effort estimation models are calibrated using data of previous projects that may belong to problem domains different from the project which is being estimated. The approach described in this thesis suggests a novel effort estimation methodology that can be used in the early stages of software development projects. The proposed methodology initially clusters the historical data from the previous projects into different problem domains and generates domain specific effort estimation models, each incorporating the impact of NFRs on effort by sets of objectively measured nominal features. The complexity of these models is reduced using a feature subset selection algorithm. In this thesis, our approach is discussed in detail, and the results of our experiments using different supervised machine learning algorithms are presented. The results show that our approach performs well by increasing the correlation coefficient and decreasing the error rate of the generated effort estimation models and achieving more accurate effort estimates for the new projects

Linguistic Approaches for Early Measurement of Functional Size from Software Requirements

The importance of early effort estimation, resource allocation and overall quality control in a software project has led the industry to formulate several functional size measurement (FSM) methods that are based on the knowledge gathered from software requirements documents. The main objective of this research is to develop a comprehensive methodology to facilitate and automate early measurement of a software's functional size from its requirements document written in unrestricted natural language. For the purpose of this research, we have chosen to use the FSM method developed by the Common Software Measurement International Consortium (COSMIC) and adopted as an international standard by the International Standardization Organization (ISO). This thesis presents a methodology to measure the COSMIC size objectively from various textual forms of functional requirements and also builds conceptual measurement models to establish traceability links between the output measurements and the input requirements. Our research investigates the feasibility of automating every major phase of this methodology with natural language processing and machine learning approaches. The thesis provides a step-by-step validation and demonstration of the implementation of this innovative methodology. It describes the details of empirical experiments conducted to validate the methodology with practical samples of textual requirements collected from both the industry and academia. Analysis of the results show that each phase of our methodology can successfully be automated and, in most cases, leads to an accurate measurement of functional size