Existing approaches to improving the robustness of source code models concentrate on recognizing adversarial samples rather than valid samples that fall outside of a given distribution, which we refer to as out-of-distribution (OOD) samples. To this end, ...

Sustainable software engineering has received a lot of attention in recent times, as we witness an ever-growing slice of energy use, for example, at data centers, as software systems utilize the underlying infrastructure. Characterizing servers for ...

Despite the recent trend of developing and applying neural source code models to software engineering tasks, the quality of such models is insufficient for real-world use. This is because there could be noise in the source code corpora used to train ...

Machine learning (ML) systems based on deep neural networks are more present than ever in software solutions for numerous industries. Their inner workings relying on models learning with data are as helpful as they are mysterious for non-expert people. ...

In the context of software development, managing and organizing agile boards of multi-disciplinary teams distributed around the world is a great challenge, especially regarding the process of assigning tickets to the correct team roles. Incorrectly ...

Linear temporal logic (LTL) satisfiability checking is a fundamental and hard (PSPACE-complete) problem. In this paper, we explore checking LTL satisfiability via end-to-end learning, so that we can take only polynomial time to check LTL satisfiability. ...

AI-enabled software systems (AIS) are prevalent in a wide range of applications, such as visual tasks of autonomous systems, extensively deployed in automotive, aerial, and naval domains. Hence, it is crucial for humans to evaluate the model’s ...

While the emerging Augmented Reality (AR) technique allows a lot of new application opportunities, from education and communication to gaming, current augmented apps often have complaints about their usability and/or user experience due to placement ...

Today, an increasing number of Adaptive Deep Neural Networks (AdNNs) are being used on resource-constrained embedded devices. We observe that, similar to traditional software, redundant computation exists in AdNNs, resulting in considerable performance ...

Debugging, that is, identifying and fixing bugs in software, is a central part of software development. Developers are therefore often confronted with the task of deciding whether a given code snippet contains a bug, and if yes, where. Recently, data-...

Despite great success in many applications, deep neural networks are not always robust in practice. For instance, a convolutional neuron network (CNN) model for classification tasks often performs unsatisfactorily in classifying some particular classes ...

Computer game is a billion-dollar industry and is booming. Testing games has been recognized as a difficult task, which mainly relies on manual playing and scripting based testing. With the advances in technologies, computer games have become ...

Creating synthetic behaviors of vehicles in simulation applications has always been challenging from a development standpoint. First, it is a real challenge to create a credible and realistic simulation while achieving the required runtime efficiency. ...

Automatically assessing code readability is a relatively new challenge that has attracted growing attention from the software engineering community. In this paper, we outline the idea to regard code readability assessment as a learning-to-rank task. ...

Scene graph generation takes an image and derives a graph representation of key objects in the image and their relations. This core computer vision task is often used in autonomous driving, where traditional software and machine learning (ML) ...

Software requirement classification is a longstanding and important problem in requirement engineering. Previous studies have applied various machine learning techniques for this problem, including Support Vector Machine (SVM) and decision trees. With ...

Many real-world online systems expect to forecast the future trend of software quality to better automate operational processes, optimize software resource cost and ensure software reliability. To achieve that, all kinds of time series metrics ...

Very large language models (LLMs), such as GPT-3 and Codex have achieved state-of-the-art performance on several natural-language tasks, and show great promise also for code. A particularly exciting aspect of LLMs is their knack for few-shot and zero-...

Self-adaptive systems increasingly rely on machine learning techniques as black-box models to make decisions even when the target world of interest includes uncertainty and unknowns. Because of the lack of transparency, adaptation decisions, as well as ...

Automated code generation is a longstanding challenge in both communities of software engineering and artificial intelligence. Currently, some works have started to investigate the functional correctness of code generation, where a code snippet is ...