Michael John Decker

Michael John Decker is an Assistant Professor at Bowling Green State University specializing in software engineering research and education. He teaches courses including Software Architecture & Design, CS Capstone, and Advanced Software Engineering from his office in Hayes 242, with research deeply integrated into source code analysis and software evolution infrastructure development.

His educational background includes a Ph.D. from Kent State University (2017) and a Master's degree from The University of Akron (2012), both in Computer Science.

Decker's research centers on software engineering with emphasis on source code analysis, program comprehension, and software maintenance/evolution. He actively develops the srcML infrastructure for syntactic representation of source code and explores natural language processing applications for identifier analysis and documentation generation. His work bridges empirical studies of developer practices with tool development to enhance code understandability and maintenance efficiency.

Analysis of his recent publications reveals consistent focus on syntactic differencing techniques, identifier semantics, and automated documentation systems. The research combines empirical validation with practical tooling, demonstrating strong trends in leveraging domain knowledge for code change comprehension and natural language integration in software artifacts.

His scientific recognition includes:

• Most Influential Paper Award at SCAM'21 (2021) for 'Lightweight Transformation and Fact Extraction with the srcML Toolkit'
• Best Challenge Entry Award at DysDoc3 for 'Automatically Redocumenting Source Code'
• $750K NSF Grant for Syntactic Differencing Infrastructure development

Decker actively secures research funding including the significant NSF grant and serves in leadership roles at major conferences (ICPC'23 Tool Track Co-Chair, ICPC'23 Session Chair). While specific student advisees aren't listed, his research involves extensive collaboration through labs and conference committees.

He is a core researcher in the Software DeveloMent Laboratory (SDML) advancing srcML infrastructure and the Source Code Analysis and Natural Language Lab (SCANL) focusing on NLP applications for source code. Current projects include syntactic differencing enhancements and identifier analysis systems for improved software evolution support.