Application of Machine Intelligence-Based Knowledge Graphs for Software Engineering

Focus Of The Chapter

The focus of the work is to throw light on knowledge graphs and their possibilities in software engineering. The exploration covers various work done in this area as follows. It covers efforts towards proposing knowledge management architecture and leveraging the knowledge graph for inferencing the documents associated with software engineering. Continuous learning agents that build on their knowledge with the flow of new information is the aspiration. Exploration of leveraging on the words and phrases of natural language with knowledge graph representation. Intelligent Integrated Development Environment enablement with knowledge graphs that work on top of artifacts associated with software development. Network analysis being leveraged for predicting defects. Feature extraction with graph mining and graph learning. Pattern recognition methodologies that work with graph matching and related techniques. Vectoral and graphical representation studies that has text mining as its base. Models for vulnerabilities prediction to optimize the testing efforts associated with software development. With these areas of exploration, the work intends to bring out the key areas that can be focused on as future research through which it is possible to strengthen the domain of software engineering. As the exploration unfolds in this work, it also throws light on the methods used to validate these experiments and assess their relevance.