Registered user since Mon 24 Jul 2017
Masud Rahman is a tenure-track Assistant Professor in the Faculty of Computer Science at Dalhousie University, Canada. He received his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan, Canada.
Masud leads the RAISE Lab at Dalhousie University. He is interested in the intelligent automation of software maintenance and evolution. He focuses on (a) a better understanding of software maintenance challenges with a particular focus on software debugging, code search, and code reviews, and (b) designing intelligent, automated, and cost-effective solutions to overcome these challenges and thus to make the developers’ lives easier. He uses a blend of Software Engineering, Machine/Deep Learning, Information Retrieval, Mining Software Repositories, Natural Language Processing and Big Data Analytics in his work. His research interests have been significantly shaped by his three years of experience as a professional developer in the software industry. He has been actively collaborating with Mozilla Firefox and Metabob Inc.
Masud’s work got accepted in several major venues of Software Engineering including ICSE, ESEC/FSE, ASE, EMSE, ICSME, and MSR. Dr. Rahman received multiple prestigious awards such as Governor General’s Gold Medal 2019, U of S Doctoral Thesis Award 2019, CS Best PhD Thesis Award 2019, and Dr Keith Geddes Award 2017 for his research excellence and outstanding academics. To date, Masud has been awarded $500K+ in competitive research funding by various agencies including NSERC Discovery Grant, Mitacs Accelerate International, NSERC Postdoctoral Fellowship, Dalhousie BELONG Program, and Dalhousie Startup Fund.
- Committee Member in New Ideas and Emerging Results Track - Program Committee within the New Ideas and Emerging Results Track-track
- Session Chair of Software Faults (part of Research Track)
- Recommending Code Reviews Leveraging Code Changes with Structured Information Retrieval
- Session Chair of Machine Learning Applications (part of Research Track)
- Bugsplainer: Leveraging Code Structures to Explain Software Bugs with Neural Machine Translation