| Automated solutions to software traceability face a difficult challenge due to the need to handle the numerous software artifacts of multiple types generated during a software life cycle, as well as the large number of relationships that exist between them. Furthermore, these artifacts are typically written using natural language making them difficult to process programmatically. Additionally, the task of searching these documents manually looking for implicit relationships is time consuming and labor intensive. This situation raises the need to automate the discovery of traceability relationships among various types of software artifacts to make the task of software traceability more feasible and cost effective.; Finding or discovering traceability relationships is the essence of the traceability problem. Once found, these relationships or "links" play important roles in various aspects of software evolution. Our research is in the area of software traceability, focusing on the issue of automatically detecting the existence of traceability links between requirements documents, design documents, and source code using machine learning. We have also conducted a study to evaluate the performance of our approach. Our evaluations allowed us to select the best classification techniques for different types of traceability links and demonstrated that our automated approach can significantly outperform humans attempting to discover traceability information manually. |
