Automatic software generation based on some specification is known as pr...
Machine programming (MP) is concerned with automating software developme...
The original "Seven Motifs" set forth a roadmap of essential methods for...
With the growth of natural language processing techniques and demand for...
Software debugging has been shown to utilize upwards of 50
time. Machine...
Class distribution skews in imbalanced datasets may lead to models with
...
Code similarity systems are integral to a range of applications from cod...
Several programming languages use garbage collectors (GCs) to automatica...
Traditional code transformation structures, such as an abstract syntax t...
The simplified parse tree (SPT) presented in Aroma, a state-of-the-art c...
Deep neural networks are increasingly being used as controllers for
safe...
A genetic algorithm (GA) attempts to solve a problem using a pool of
pot...
Program synthesis using inputs and outputs is a fundamental problem in
c...
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
In this position paper, we describe our vision of the future of machine
...
In this position paper, we describe our vision of the future of machine-...
Classical anomaly detection (AD) is principally concerned with point-bas...
The increasing use of deep neural networks for safety-critical applicati...
Classical anomaly detection is principally concerned with point-based
an...
This short paper describes our ongoing research on Greenhouse - a
zero-p...
In this paper, we present Paranom, a parallel anomaly dataset generator....
In this paper, we present AutoPerf, a generalized software performance
a...
In this paper, we present the first-of-its-kind machine learning (ML) sy...