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- research-articleJuly 2024
Less Cybersickness, Please: Demystifying and Detecting Stereoscopic Visual Inconsistencies in Virtual Reality Apps
Proceedings of the ACM on Software Engineering (PACMSE), Volume 1, Issue FSEArticle No.: 96, Pages 2167–2189https://doi.org/10.1145/3660803The quality of Virtual Reality (VR) apps is vital, particularly the rendering quality of the VR Graphical User Interface (GUI). Different from traditional two-dimensional (2D) apps, VR apps create a 3D digital scene for users, by rendering two distinct ...
- research-articleJune 2024
RAPID: Zero-Shot Domain Adaptation for Code Search with Pre-Trained Models
ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 33, Issue 5Article No.: 128, Pages 1–35https://doi.org/10.1145/3641542Code search, which refers to the process of identifying the most relevant code snippets for a given natural language query, plays a crucial role in software maintenance. However, current approaches heavily rely on labeled data for training, which results ...
- research-articleMay 2024
ReposVul: A Repository-Level High-Quality Vulnerability Dataset
ICSE-Companion '24: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion ProceedingsApril 2024, Pages 472–483https://doi.org/10.1145/3639478.3647634Open-Source Software (OSS) vulnerabilities bring great challenges to the software security and pose potential risks to our society. Enormous efforts have been devoted into automated vulnerability detection, among which deep learning (DL)-based approaches ...
- short-paperMay 2024
Imperfect Code Generation: Uncovering Weaknesses in Automatic Code Generation by Large Language Models
ICSE-Companion '24: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion ProceedingsApril 2024, Pages 422–423https://doi.org/10.1145/3639478.3643081The task of code generation has received significant attention in recent years, especially when the pre-trained large language models (LLMs) for code have consistently achieved state-of-the-art performance. However, there is currently a lack of a ...
- research-articleMay 2024JUST ACCEPTED
Meta-Learning for Multi-Family Android Malware Classification
ACM Transactions on Software Engineering and Methodology (TOSEM), Just Accepted https://doi.org/10.1145/3664806With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users’ devices and private data. Many ...
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- research-articleApril 2024
Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models
ICSE '24: Proceedings of the IEEE/ACM 46th International Conference on Software EngineeringMay 2024, Article No.: 80, Pages 1–13https://doi.org/10.1145/3597503.3639216Pre-trained code models have recently achieved substantial improvements in many code intelligence tasks. These models are first pre-trained on large-scale unlabeled datasets in a task-agnostic manner using self-supervised learning, and then fine-tuned on ...
- research-articleApril 2024
On Extracting Specialized Code Abilities from Large Language Models: A Feasibility Study
ICSE '24: Proceedings of the IEEE/ACM 46th International Conference on Software EngineeringMay 2024, Article No.: 74, Pages 1–13https://doi.org/10.1145/3597503.3639091Recent advances in large language models (LLMs) significantly boost their usage in software engineering. However, training a well-performing LLM demands a substantial workforce for data collection and annotation. Moreover, training datasets may be ...
- research-articleApril 2024
Code Search is All You Need? Improving Code Suggestions with Code Search
ICSE '24: Proceedings of the IEEE/ACM 46th International Conference on Software EngineeringMay 2024, Article No.: 73, Pages 1–13https://doi.org/10.1145/3597503.3639085Modern integrated development environments (IDEs) provide various automated code suggestion techniques (e.g., code completion and code generation) to help developers improve their efficiency. Such techniques may retrieve similar code snippets from the ...
- research-articleApril 2024
Less is More? An Empirical Study on Configuration Issues in Python PyPI Ecosystem
ICSE '24: Proceedings of the IEEE/ACM 46th International Conference on Software EngineeringMay 2024, Article No.: 202, Pages 1–12https://doi.org/10.1145/3597503.3639077Python is the top popular programming language used in the open-source community, largely owing to the extensive support from diverse third-party libraries within the PyPI ecosystem. Nevertheless, the utilization of third-party libraries can potentially ...
- research-articleMarch 2024
Prompt enhance API recommendation: visualize the user’s real intention behind this query
Automated Software Engineering (KLU-AUSE), Volume 31, Issue 1May 2024https://doi.org/10.1007/s10515-024-00425-0AbstractDevelopers frequently rely on APIs in their daily programming tasks, as APIs have become an indispensable tool for program development. However, with a vast number of open-source libraries available, selecting the appropriate API quickly can be a ...
- research-articleFebruary 2024
Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors
ICSE '24: Proceedings of the IEEE/ACM 46th International Conference on Software EngineeringMay 2024, Article No.: 4, Pages 1–13https://doi.org/10.1145/3597503.3608132As a dynamic programming language, Python has become increasingly popular in recent years. Although the dynamic type system of Python facilitates the developers in writing Python programs, it also brings type errors at run-time which are prevalent yet ...
- research-articleNovember 2023
How Practitioners Expect Code Completion?
ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software EngineeringNovember 2023, Pages 1294–1306https://doi.org/10.1145/3611643.3616280Code completion has become a common practice for programmers during their daily programming activities. It automatically predicts the next tokens or statements that the programmers may use. Code completion aims to substantially save keystrokes and ...
- research-articleNovember 2023
A Unified Framework for Mini-game Testing: Experience on WeChat
ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software EngineeringNovember 2023, Pages 1623–1634https://doi.org/10.1145/3611643.3613868Mobile games play an increasingly important role in our daily life. The quality of mobile games can substantially affect the user experience and game revenue. Different from traditional mobile games, the mini-games provided by our partner, Tencent, are ...
- research-articleNovember 2023
TopicAns: Topic-informed Architecture for Answer Recommendation on Technical Q&A Site
ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 33, Issue 1Article No.: 20, Pages 1–25https://doi.org/10.1145/3607189Technical Q&A sites, such as Stack Overflow and Ask Ubuntu, have been widely utilized by software engineers to seek support for development challenges. However, not all the raised questions get instant feedback, and the retrieved answers can vary in ...
- research-articleNovember 2023
Protecting Intellectual Property of Large Language Model-Based Code Generation APIs via Watermarks
CCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications SecurityNovember 2023, Pages 2336–2350https://doi.org/10.1145/3576915.3623120The rise of large language model-based code generation (LLCG) has enabled various commercial services and APIs. Training LLCG models is often expensive and time-consuming, and the training data are often large-scale and even inaccessible to the public. ...
- research-articleOctober 2023
Prompt Tuning in Code Intelligence: An Experimental Evaluation
IEEE Transactions on Software Engineering (ISOF), Volume 49, Issue 11Nov. 2023, Pages 4869–4885https://doi.org/10.1109/TSE.2023.3313881Pre-trained models have been shown effective in many code intelligence tasks, such as automatic code summarization and defect prediction. These models are pre-trained on large-scale unlabeled corpus and then fine-tuned in downstream tasks. However, as the ...
- research-articleOctober 2023
Dialog summarization for software collaborative platform via tuning pre-trained models
Journal of Systems and Software (JSSO), Volume 204, Issue COct 2023https://doi.org/10.1016/j.jss.2023.111763AbstractSoftware collaborative platforms, e.g., Gitter live chat and GitHub Discussions, are essential in software maintenance. Summarizing the live chat logs is useful for extracting, retrieving, and sharing knowledge for software developers. Automatic ...
Highlights- Dialog Disentanglement and summarization for software collaborative platform.
- Using the prompt tuning technique to exploit the knowledge of the pre-trained models for the summary tasks.
- Presenting manual annotation tool consisting ...
- research-articleAugust 2023
Health warning based on 3R ECG Sample's combined features and LSTM
Computers in Biology and Medicine (CBIM), Volume 162, Issue CAug 2023https://doi.org/10.1016/j.compbiomed.2023.107082AbstractMost researches use the fixed-length sample to identify ECG abnormalities based on MIT ECG dataset, which leads to information loss. To address this problem, this paper proposes a method for ECG abnormality detection and health warning based on ...
Highlights- High-quality ECG signal selection based on dual threshold and fluctuation value.
- Variable-length 3R ECG sample extraction adaptive to different heart rates.
- Multi-domain combination features including sub-band spectrum and harmonic ...
- research-articleJuly 2023
Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning
ICSE '23: Proceedings of the 45th International Conference on Software EngineeringMay 2023, Pages 2275–2286https://doi.org/10.1109/ICSE48619.2023.00191Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in learning the graph representations of source code and are commonly adopted by ...
- research-articleJuly 2023
Two Sides of the Same Coin: Exploiting the Impact of Identifiers in Neural Code Comprehension
ICSE '23: Proceedings of the 45th International Conference on Software EngineeringMay 2023, Pages 1933–1945https://doi.org/10.1109/ICSE48619.2023.00164Previous studies have demonstrated that neural code comprehension models are vulnerable to identifier naming. By renaming as few as one identifier in the source code, the models would output completely irrelevant results, indicating that identifiers ...