Prem Devanbu holds a degree in EE from IIT Madras, and a Ph.D in CS from Rutgers University. After some decades at Bell Labs, he joined UC Davis, where is now Distinguished Research Professor of Computer Science. His early work on GENOA (a general-purpose analysis tool for code) led to an enduring passion for finding ways to improve the productivity, reliability and quality of practical software systems. Between 2000 and 2005, he worked on the use of Merkle hash trees (aka “blockchains”) for secure data out-sourcing. Starting around 2005, his work shifted to study the copious amounts of time-series data available in open-source repositories: how can this data be used to help improve software tools and processes? Devanbu has published several test-of-time award-winning papers which studied various aspects of the Data Science of software-related data, including: developer social networks, data quality, and modeling challenges.
In 2012, Hindle, Barr, Su, and Devanbu published their “Naturalness of Software” work, introducing the notion that language models can effectively model source code. Subsequent work showed that discrete language models, customized for source code, worked for several tasks, including both code completion and code de-obfuscation. This work included a nested cache model, which could beat contemporaneous DNN auto-regressive models. Around 2017, working with Earl Barr and others, Devanbu realized that code is bimodal, allowing both algorithmic static analysis and statistical modeling. This has led to a line of work of bimodal approaches to training, pre-training, and prompt-engineering, for applications such as syntax error correction, code summarization, code repair, and code completion. Most recently, Devanbu’s work has been directed at helping human developers make better, safer use of output from language models. Devanbu has won the ACM SIGSOFT Outstanding Research Award, and the Alexander von Humboldt Research Prize. He is an ACM Fellow.
