SMARTKT: A Search Framework to Assist Program Comprehension using Smart Knowledge Transfer

Abstract

Regardless of attempts to extract knowledge from code bases to aid in program comprehension, there is an absence of a framework to extract and integrate knowledge to provide a near-complete multifaceted understanding of a program. To bridge this gap, we propose SMARTKT (Smart Knowledge Transfer) to extract and transfer knowledge related to software development and application-specific characteristics and their interrelationships in form of a knowledge graph. For an application, the knowledge graph provides an overall understanding of the design and implementation and can be used by an intelligent natural language query system to convert the process of knowledge transfer into a developer-friendly Google-like search. For validation, we develop an analyzer to discover concurrency-related design aspects from runtime traces in a machine learning framework and obtain a precision and recall of around 97% and 95% respectively. We extract application-specific knowledge from code comments and obtain 72% match against human-annotated ground truth.