Application of Machine Intelligence-Based Knowledge Graphs for Software Engineering

Application of Machine Intelligence-Based Knowledge Graphs for Software Engineering

Raghavendra Rao Althar (First American India, India & Christ University (Deemed), India) and Debabrata Samanta (Christ University (Deemed), India)
DOI: 10.4018/978-1-7998-7701-1.ch010
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This chapter focuses on knowledge graphs application in software engineering. It starts with a general exploration of artificial intelligence for software engineering and then funnels down to the area where knowledge graphs can be a good fit. The focus is to put together work done in this area and call out key learning and future aspirations. The knowledge management system's architecture, specific application of the knowledge graph in software engineering like automation of test case creation and aspiring to build a continuous learning system are explored. Understanding the semantics of the knowledge, developing an intelligent development environment, defect prediction with network analysis, and clustering of the graph data are exciting explorations.
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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.

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