The three pillars of machine programming

J Gottschlich, A Solar-Lezama, N Tatbul… - Proceedings of the 2nd …, 2018 - dl.acm.org
In this position paper, we describe our vision of the future of machine programming through
a categorical examination of three pillars of research. Those pillars are:(i) intention,(ii) …

[PDF][PDF] Greenhouse: A zero-positive machine learning system for time-series anomaly detection

TJ Lee, J Gottschlich, N Tatbul, E Metcalf… - arXiv preprint arXiv …, 2018 - arxiv.org
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection
Page 1 Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly …

Foreseer: Efficiently forecasting malware event series with long short-term memory

K Gogineni, P Derasari… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
With the rise in malware attacks on modern computer systems, there is a critical need for the
computer security defenses to not only detect the presence of adversaries accurately, but …

Inferring performance bug patterns from developer commits

Y Chen, S Winter, N Suri - 2019 IEEE 30th international …, 2019 - ieeexplore.ieee.org
Performance bugs, ie, program source code that is unnecessarily inefficient, have received
significant attention by the research community in recent years. A number of empirical …

Paranom: A parallel anomaly dataset generator

J Gottschlich - arXiv preprint arXiv:1801.03164, 2018 - arxiv.org
Paranom: A Parallel Anomaly Dataset Generator Page 1 Paranom: A Parallel Anomaly Dataset
Generator Justin Gottschlich Intel Labs ABSTRACT In this paper, we present Paranom, a parallel …