  As the role of information and communication technologies gradually increases
in our lives, source code security becomes a significant issue to protect
against malicious attempts Furthermore with the advent of data-driven
techniques, there is now a growing interest in leveraging machine learning and
natural language processing as a source code assurance method to build
trustworthy systems Therefore training our future software developers to write
secure source code is in high demand In this thesis we propose a framework
including learning modules and hands on labs to guide future IT professionals
towards developing secure programming habits and mitigating source code
vulnerabilities at the early stages of the software development lifecycle In
this thesis our goal is to design learning modules with a set of hands on labs
that will introduce students to secure programming practices using source code
and log file analysis tools to predict and identify vulnerabilities In a Secure
Coding Education framework we will improve students skills and awareness on
source code vulnerabilities detection tools and mitigation techniques integrate
concepts of source code vulnerabilities from Function API and library level to
bad programming habits and practices leverage deep learning NLP and static
analysis tools for log file analysis to introduce the root cause of source code
vulnerabilities
