Machine programming (MP) is a new field of research that uses automation to improve software development productivity (e.g., the time it takes a developer to write code) and quality (e.g., performance, correctness, security, maintainability, etc.). We generally consider MP as a fusion of machine learning and formal methods, which rely heavily on programming languages and systems.

Follow

Submissions from 2020

PDF

Learned Garbage Collection, Lujing Cen, Ryan Marcus, Hongzi Mao, Justin E. Gottschlich, Mohammad Alizadeh, and Tim Kraska

PDF

An Abstraction-Based Framework for Neural Network Verification, Yizhak Y. Elboher, Justin E. Gottschlich, and Guy Katz

PDF

ControlFlag: A Self-supervised Idiosyncratic Pattern Detection System for Software Control Structures, Niranjan Hasabnis and Justin E. Gottschlich

PDF

Software Language Comprehension using a Program-Derived Semantics Graph, Roshni G. Iyer, Yizhou Sun, Wei Wang, and Justin E. Gottschlich

PDF

MISIM: A Novel Code Similarity System, Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin J. Tithi, Niranjan Hasabnis, Paul Petersen, Timothy Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, and Justin E. Gottschlich

Submissions from 2019

PDF

A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions, Mejbah Alam, Justin E. Gottschlich, Nesime Tatbul, Javier S. Turek, Timothy Mattson, and Abdullah Muzahid

PDF

MLSys: The New Frontier of Machine Learning Systems, Alexander Ratner, Dan Alistarh, Gustavo Alons, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin E. Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecny, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H B. McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Christopher Re, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew G. Wilson, Eric Xing, Matei Zaharia, Ce Zhang, and Ameet Talwalkar

Submissions from 2018

PDF

The Three Pillars of Machine Programming, Justin E. Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman Amarasinghe, Joshua B. Tenenbaum, and Timothy Mattson

PDF

Toward Scalable Verification for Safety-Critical Deep Networks, Lindsey Kuper, Guy Katz, Justin E. Gottschlich, Kyle Julian, Clark Barrett, and Mykel J. Kochenderfer

PDF

Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection, Tae J. Lee, Justin E. Gottschlich, Nesime Tatbul, Eric Metcalf, and Stan Zdonik

PDF

Precision and Recall for Range-Based Anomaly Detection, Tae J. Lee, Justin E. Gottschlich, Nesime Tatbul, Eric Metcalf, and Stan Zdonik

PDF

Precision and Recall for Time Series, Nesime Tatbul, Tae J. Lee, Stan Zdonik, Mejbah Alam, and Justin E. Gottschlich