Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

Testing Autonomous Driving Systems (ADSs) is a critical task for ensuring the reliability and
safety of autonomous vehicles. Existing methods mainly focus on searching for safety …

Code completion, a highly valuable topic in the software development domain, has been
increasingly promoted for use by recent advances in large language models (LLMs). To …

Autonomous vehicle (AV) systems must be comprehensively tested and evaluated before
they can be deployed. High-fidelity simulators such as CARLA or LGSVL allow this to be …

The prosperous trend of deploying deep neural network (DNN) models to diverse hardware
platforms has boosted the development of deep learning (DL) compilers. DL compilers take …

Visual question answering (VQA) takes an image and a natural-language question as input
and returns a natural-language answer. To date, VQA models are primarily assessed by …

Neural program embeddings have demonstrated considerable promise in a range of
program analysis tasks, including clone identification, program repair, code completion, and …

Deep neural network (DNN) testing approaches have grown fast in recent years to test the
correctness and robustness of DNNs. In particular, DNN coverage criteria are frequently …

Question Answering (QA) is an attractive and challenging area in NLP community. There are
diverse algorithms being proposed and various benchmark datasets with different topics and …

Image captioning (IC) systems, which automatically generate a text description of the salient
objects in an image (real or synthetic), have seen great progress over the past few years due …