Deep learning (DL) components have been broadly applied in diverse applications. Similar to traditional software engineering, effective test case generation methods are needed by industry to enhance the quality and robustness of these deep learning ...

Online service systems struggle with accurately and quickly pinpointing and resolving failures within their intricate systems, and it therefore emerges the solutions for fault localization in the code. However, the previous fault localization models ...

Large language models (LLMs) demonstrate impressive capabilities to generate accurate code snippets given natural language intents in a zero-shot manner, i.e., without the need for specific fine-tuning. While prior studies have highlighted the advantages ...

Recommending API sequences is crucial in software development, saving developers time and effort. While previous studies primarily focus on accuracy, often recommending popular APIs, they tend to overlook less frequent, or ‘tail,’ APIs. This oversight, ...

Large Language Models (LLMs) have shown impressive abilities in code generation. Chain-of-Thought (CoT) prompting is the state-of-the-art approach to utilizing LLMs. CoT prompting asks LLMs first to generate CoTs (i.e., intermediate natural language ...

Quantum Neural Network (QNN) combines the deep learning (DL) principle with the fundamental theory of quantum mechanics to achieve machine learning tasks with quantum acceleration. Recently, QNN systems have been found to manifest robustness issues ...

This article represents the Replicated Computational Results (RCR) related to our TOSEM paper “A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms,” where we proposed LAFF, an approach to automatically suggest ...

The competitive game between agents exists in many critical applications, such as military unmanned aerial vehicles. It is urgent to test these agents to reduce the significant losses caused by their failures. Existing studies mainly are to construct a ...

Neural networks are important computational models used in the domains of artificial intelligence and software engineering. Parameters of a neural network are obtained via training it against a specific dataset with a standard process, which guarantees ...

Operation and maintenance are critical activities in the whole life cycle of modern online software systems, and anomaly detection is a crucial step of these activities. Recent studies mainly develop deep learning techniques to complete this task. Notably,...

Bindings for machine learning frameworks (such as TensorFlow and PyTorch) allow developers to integrate a framework’s functionality using a programming language different from the framework’s default language (usually Python). In this article, we study ...

Background: Recently, machine and deep learning (ML/DL) algorithms have been increasingly adopted in many software systems. Due to their inductive nature, ensuring the quality of these systems remains a significant challenge for the research community. ...

In this paper, we provide the first systematic literature review of the intersection of two research areas, Multi-Label Learning (MLL) and Software Engineering (SE). We refer to this intersection as MLL4SE. In recent years, MLL problems have increased in ...

Trusted Execution Environments (TEE) are used to safeguard on-device models. However, directly employing TEEs to secure the entire DNN model is challenging due to the limited computational speed. Utilizing GPU can accelerate DNN's computation speed but ...

As automated speech recognition (ASR) systems gain widespread acceptance, there is a pressing need to rigorously test and enhance their performance. Nonetheless, the process of collecting and executing speech test cases is typically both costly and time-...

As software systems evolve to meet the changing needs of users, understanding the source code becomes a critical step in the process. Clustering techniques, also known as modularization techniques, offer a solution to breaking down complex source code ...

Uninterpreted predicate solving is a fundamental problem in formal verification, including loop invariant and constrained horn clauses predicate solving. Existing approaches have been mostly in symbolic ways. While achieving sustainable progress, they ...

To foster the verifiability and testability of deep neural networks (DNN), an increasing number of methods for test case generation techniques are being developed.

When confronted with testing DNN models, the user can apply any existing test generation ...

Convolutional neural networks (CNNs) have been widely used in various fields. However, it is essential to perform sufficient testing to detect internal defects before deploying CNNs, especially in security-sensitive scenarios. Generating error-inducing ...

Large language models (LLMs) have shown great success in code generation. LLMs take as the input a prompt and output the code. How to make prompts (i.e., Prompting Techniques) is a key question. Existing prompting techniques are designed for natural ...