89 citations · 118 across the 6 of their papers we have counts for
6 papers · 1 filter
Neural Software Analysis
Michael Pradel, Satish Chandra
Many software development problems can be addressed by program analysis tools, which traditionally are based on precise, logical reasoning and heuristics to ensure that the tools a…
Satisfying Increasing Performance Requirements with Caching at the Application Level
Jhonny Mertz, Ingrid Nunes, Luca Della Toffola +2
Application-level caching is a form of caching that has been increasingly adopted to satisfy performance and throughput requirements. The key idea is to store the results of a comp…
TypeWriter: Neural Type Prediction with Search-based Validation
Michael Pradel, Georgios Gousios, Jason Liu +1
Maintaining large code bases written in dynamically typed languages, such as JavaScript or Python, can be challenging due to the absence of type annotations: simple data compatibil…
Neural Bug Finding: A Study of Opportunities and Challenges
Andrew Habib, Michael Pradel
Static analysis is one of the most widely adopted techniques to find software bugs before code is put in production. Designing and implementing effective and efficient static analy…
Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts
Rohan Bavishi, Michael Pradel, Koushik Sen
Most of the JavaScript code deployed in the wild has been minified, a process in which identifier names are replaced with short, arbitrary and meaningless names. Minified code occu…
DeepBugs: A Learning Approach to Name-based Bug Detection
Michael Pradel, Koushik Sen
Natural language elements in source code, e.g., the names of variables and functions, convey useful information. However, most existing bug detection tools ignore this information…