PLDI 2022 (series) / MAPS 2022 (series) / The 6th Annual Symposium on Machine Programming / Improving Software Reliability using Machine Learning
Improving Software Reliability using Machine Learningvirtual
Mon 13 Jun 2022 10:30 - 11:15 at Boardroom - Morning II Chair(s): Satish Chandra
Mon 13 Jun 2022 22:30 - 23:15 at Boardroom - Morning II
Mon 13 Jun 2022 22:30 - 23:15 at Boardroom - Morning II
Software bugs cost millions of dollars to the US economy. Improving software reliability has been one of the primary concerns of Software Engineering, Security, Programming Language, and Verification research over decades. Researchers developed numerous automatic bug-finding and bug-fixing tools, either based on static code analysis or analyzing dynamic code behavior. However, the adoption of these methods in the real world is still limited, partly because most of them require a significant amount of manual work from developers and have a steep learning curve. This talk will discuss how machine learning-based approaches can help us automate and scale up the bug-finding and bug-fixing process for large real-world programs.
Mon 13 JunDisplayed time zone: Pacific Time (US & Canada) change
Mon 13 Jun
Displayed time zone: Pacific Time (US & Canada) change
10:30 - 12:00 | |||
10:30 45mKeynote | Improving Software Reliability using Machine Learningvirtual MAPS Baishakhi Ray Columbia University | ||
11:15 15mTalk | Productivity Assessment of Neural Code Completion MAPS Albert Ziegler GitHub, Eirini Kalliamvakou GitHub, X. Alice Li GitHub, Andrew Rice GitHub, Devon Rifkin GitHub, Shawn Simister GitHub, Ganesh Sittampalam GitHub, Eddie Aftandilian GitHub Pre-print | ||
11:30 15mTalk | Predictive Synthesis of API-Centric Code MAPS Daye Nam CMU, Carnegie Mellon University, Baishakhi Ray Columbia University, Seohyun Kim Meta, xianshan qu , Satish Chandra Facebook Pre-print | ||
11:45 15mTalk | From Perception to Programs: Regularize, Overparameterize, and Amortize MAPS | ||
22:30 - 00:00 | |||
22:30 45mKeynote | Improving Software Reliability using Machine Learningvirtual MAPS Baishakhi Ray Columbia University | ||
23:15 15mTalk | Productivity Assessment of Neural Code Completion MAPS Albert Ziegler GitHub, Eirini Kalliamvakou GitHub, X. Alice Li GitHub, Andrew Rice GitHub, Devon Rifkin GitHub, Shawn Simister GitHub, Ganesh Sittampalam GitHub, Eddie Aftandilian GitHub Pre-print | ||
23:30 15mTalk | Predictive Synthesis of API-Centric Code MAPS Daye Nam CMU, Carnegie Mellon University, Baishakhi Ray Columbia University, Seohyun Kim Meta, xianshan qu , Satish Chandra Facebook Pre-print | ||
23:45 15mTalk | From Perception to Programs: Regularize, Overparameterize, and Amortize MAPS | ||
