Modern language models (LMs) have been successfully employed in source code
generation and understanding, leading to a significant increase in research focused on …

The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in
Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment …

Machine learning (ML) models play an increasingly prevalent role in many software
engineering tasks. However, because most models are now powered by opaque deep …

Vulnerability detection is crucial for ensuring the security and reliability of software systems.
Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding …

This article presents a comprehensive survey on test optimization in deep neural network
(DNN) testing. Here, test optimization refers to testing with low data labeling effort. We …

This article focuses on the problem of optimizing the system utility of Machine Learning (ML)-
based systems in the presence of ML mispredictions. This is achieved via the use of self …

Interpreting and debugging machine learning models is necessary to ensure the robustness
of the machine learning models. Explaining mispredictions can help significantly in doing so …

Code comment, ie, the natural language text to describe the semantic of a code snippet, is
an important way for developers to comprehend the code. Recently, a number of …

In this work, we study literature in Explainable AI and Safe AI to understand poisoning of
neural models of code. In order to do so, we first establish a novel taxonomy for Trojan AI for …

Deep learning models are trained with certain assumptions about the data during the
development stage and then used for prediction in the deployment stage. It is important to …