Knowledge Graph based Automated Generation of Test Cases in Software Engineering

Abstract

Knowledge Graph (KG) is extremely efficient in storing and retrieving information from data that contains complex relationships between entities. Such a representation is relevant in software engineering projects, which contain large amounts of inter-dependencies between classes, modules, functions etc. In this paper, we propose a methodology to create a KG from software engineering documents that will be used for automated generation of test cases from natural (domain) language requirement statements. We propose a KG creation tool that includes a novel Constituency Parse Tree (CPT) based path finding algorithm for test intent extraction, Conditional Random field (CRF) based Named Entity Recognition (NER) model with automatic feature engineering and a Sentence vector embedding based signal extraction. This paper demonstrates the contributions on an automotive domain software project.