News

Check out PRECISE's progress towards assuredly safer autonomous systems.

VentureBeat published an article on Intel’s research, prominently featuring Dr. Gottschlich and his Machine Programming Research team at NeurIPS '19.

Justin Gottschlich was recently featured in the November issue of The Economic Times (India Times).  That article compliments Dr. Gottschlich's insights and expertise on "Machine Learning" and "Machine Programming" from his Knowledge@Wharton interview.

Machine Programming: What Lies Ahead?

Eric Micallef, a senior in the Embedded Systems (EMBS) MSE program, is one of the 2019 recipients of the J.P. Eckert Master’s Fellowship. Congratulations, Eric!

PRECISE members Insup Lee and Oleg Sokolsky, along with Sampath Kannan, Moonzoo Kim, and Mahesh Viswanathan, win the Test-of-Time Award for their 2001 paper titled "Java-MaC: a run-time assurance tool for Java programs" at the 2019 Conference on Runtime Verification.

Luan Nguyen, Gautam Mohan, James Weimer, Oleg Sokolsky, Insup Lee and Rajeev Alur won Best Paper Award on their "Detecting Security Leaks in Hybrid Systems with Information Flow Analysis" at ACM-IEEE International Conference on Formal Methods and Models for System Design (MEMOCODE), 2019.  

Congratulations to Luan and all!

Philly Inquirer article on Uber Elevate quotes our "Fly-by-Logic" research.

Kihong Heo, Mukund Raghothaman (soon to join USC CS), Xujie Si, and Mayur Naik are the recipients of the Distinguished Paper Award at the Conference on Programming Language Design and Implementation (PLDI 2019) for their paper "Continuously Reasoning about Programs using Differential Bayesian Inference".  Their abstract could be found at https://www.youtube.com/watch?v=TebtiCG4HCA

Software developers are struggling to manage the rapidly growing size and complexity of the systems they develop, and are increasingly looking to sophisticated automatic techniques to help them improve the quality of their code. Static analysis is one such popular technique, and promises to automatically find bugs in software before it is even run. Recent years have witnessed the widespread adoption of static analysis tools by industry, from aerospace and safety-critical medical applications to security-conscious cloud service providers.  Unfortunately, the thoroughness of static analyzers is a double-edged sword, and developers are often overwhelmed by the number of warnings produced by tools. In their paper, Dr. Naik, with his postdocs Kihong Heo and Mukund Raghothaman, and his Ph.D.c Xujie Si, developed an AI-based system to prioritize these alarms based on how relevant they are to the developers’ immediate changes. Their system, Drake, will be more effective at identifying vulnerabilities, and promises to eliminate defects as soon as they are introduced into the code-base.  Our PRECISE team plans on deploying Drake on commercial software hosting services such as GitHub, in the coming months.

Two of Rajeev Alur's doctoral students, Konstantinos Mamouras (now at Rice) and Arjun Radhakrishna (now with Microsoft), presented their work at PLDI 2019.  

Links to their papers:

Arun Iyer, Manohar Jonnalagedda, Suresh Parthasarathy, Arjun Radhakrishna, Sriram K. Rajamani: Synthesis and Machine Learning for Heterogeneous Extraction.

Konstantinos Mamouras, Caleb Stanford, Rajeev Alur, Zachary G. Ives, Val Tannen: Data-Trace Types for Distributed Stream Processing Systems.