Software Systems

Designing a suitable representation for code-reasoning tasks is challenging in aspects such as the kinds of program information to model, how to combine them, and how much context to consider. We propose CodeTrek, a deep learning approach that addresses these challenges by representing codebases as databases that conform to rich relational schemas. The relational representation not only allows...

Information-rich relational graphs have shown great potential in designing effective representations of code for program-understanding tasks. However, the wealth of structural and semantic information in such graphs can overwhelm models, because of their limited input size. A promising approach for overcoming this challenge is to gather presumed-relevant but smaller context from a larger graph,...

When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neural program synthesis methods have similar capabilities, what we can measure is whether they compositionally generalize, that is, whether a model that has been trained on the simpler subtasks is subsequently able to...

Optimizing the implementation of tensor computations is essential to exploiting the full capacity of a given processor architecture on a wide range of scientific and machine learning applications. However, the complexity of the microarchitectural features that come into play when approaching the peak performance of the processor makes it very hard. Focusing on 2D convolutions, we observe a...