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Received three-year NSF research grant on imperative Deep Learning program robustness and evolution as PI
I am pleased to announce that I, along with co-PI Anita Raja, have received a three-year standard research grant from the National Science Foundation (NSF) Software & Hardware Foundations (SHF) program as principal investigator (PI) for a project entitled “Practical Analyses and Safe Transformations for Imperative Deep Learning Programs.” The total grant amount is $600K.
The project will facilitate the robustness and automated evolution and maintenance of large, industrial Deep Learning (DL) software systems that use imperative style programming. More information may be found on NSF’s website; stay tuned for more details and funded research opportunities!
Paper on feature log level rejuvenation accepted to Science of Computer Programming
I am pleased to announce that our journal paper, “Automated Evolution of Feature Logging Statement Levels Using Git Histories and Degree of Interest,” has been recommended for publication in Science of Computer Programming. Congrats to Yiming, Allan, and Mehdi!
Invited to IEEE SCAM ’21 PC
I have been invited to serve on the research track program committee (PC) of the 2021 IEEE International Working Conference on Source Code Analysis and Manipulation (IEEE SCAM), colocated with ICSME 2021.
(more…)Slides for Automated Refactoring of Legacy Java Software to Default Methods Talk at ICSE 2017
Slides for our talk on default method refactoring at ICSE 2017 are now available on slideshare. (more…)
