Writing tests is a time-consuming yet essential task during software development. We
propose to leverage recent advances in deep learning for text and code generation to assist …

Testing to detect semantic bugs is essential, especially for critical systems. Coverage-guided
fuzzing (CGF) and runtime assertion checking (RAC) are two well-known approaches for …

Neural networks have complex structures, and thus it is hard to understand their inner
workings and ensure correctness. To understand and debug convolutional neural networks …

Much research on automated program debugging often assumes that bug fix location (s)
indicate the faults' root causes and that root causes of faults lie within single code elements …

We recently proposed inline tests for validating individual program statements; they allow
developers to provide test inputs, expected outputs, and test oracles immediately after a …

Bounded exhaustive input generation (BEG) is an effective approach to reveal software
faults. However, existing BEG approaches require a precise specification of the valid inputs …

Software testing and verification are essential for keeping software systems reliable and safe
to use. However, it requires significant manual effort to write and maintain code artifacts …

Deep learning compilers help address difficulties of deploying deep learning models on
diverse types of hardware. Testing deep learning compilers is highly crucial, because they …

Abstract Machine Learning (ML) is rapidly revolutionizing the way modern-day systems are
developed. However, testing ML-based systems is challenging due to 1) the presence of …