Support for traceability management of software artefacts using Natural Language Processing

One of the major problems in software development process is managing software artefacts. While software evolves, inconsistencies between the artefacts do evolve as well. To resolve the inconsistencies in change management, a tool named “Software Artefacts Traceability Analyzer (SAT-Analyzer)” was introduced as the previous work of this research. Changes in software artefacts in requirement specification, Unified Modelling Language (UML) diagrams and source codes can be tracked with the help of Natural Language Processing (NLP) by creating a structured format of those documents. Therefore, in this research we aim at adding an NLP support as an extension to SAT-Analyzer. Enhancing the traceability links created in the SAT-analyzer tool is another focus due to artefact inconsistencies. This paper includes the research methodology and relevant research carried out in applying NLP for improved traceability management. Tool evaluation with multiple scenarios resulted in average Precision 72.22%, Recall 88.89% and F1 measure of 78.89% suggesting high accuracy for the domain.