Abstract:
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 (SATAnalyzer)”
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.
Citation:
A. Arunthavanathan et al., "Support for traceability management of software artefacts using Natural Language Processing," 2016 Moratuwa Engineering Research Conference (MERCon), 2016, pp. 18-23, doi: 10.1109/MERCon.2016.7480109.