A5100326214- Software Engineering Research
- Software Reliability and Analysis Research
- Machine Learning and Data Classification
- Software Testing and Debugging Techniques
- Advanced Malware Detection Techniques
- Adversarial Robustness in Machine Learning
- Topic Modeling
- Natural Language Processing Techniques
- Web Data Mining and Analysis
- Software System Performance and Reliability
- Quality and Safety in Healthcare
- Real-time simulation and control systems
- Mobile Crowdsensing and Crowdsourcing
- Scientific Computing and Data Management
- Safety Systems Engineering in Autonomy
- Radiomics and Machine Learning in Medical Imaging
- Artificial Intelligence in Law
- Head and Neck Cancer Studies
- Hate Speech and Cyberbullying Detection
- Artificial Intelligence in Healthcare and Education
- Advanced Neural Network Applications
- Ferroelectric and Negative Capacitance Devices
- AI and Big Data Applications
- Engineering Education and Technology
- Semiconductor materials and devices
York University
2021-2025
Lanzhou Jiaotong University
2024
Hunan Cancer Hospital
2023
Central South University
2023
Lyceum of the Philippines University
2023
East University Of Heilongjiang
2022
Southeast University
2022
Taiwan Semiconductor Manufacturing Company (United States)
2010
Hewlett-Packard (United States)
2009
A high performance 22/20nm CMOS bulk FinFET achieves the best in-class N/P I<inf>on</inf> values of 1200/1100 µA/µm for I<inf>off</inf>=100nA/µm at 1V. Excellent device electrostatic control is demonstrated gate length (L<inf>gate</inf>) down to 20nm. Dual-Epitaxy and multiple stressors are essential boost performance. Dual workfunction (WF) with an advanced High-K/Metal (HK/MG) stack deployed in integration-friendly process flow. This dual-WF approach provides excellent...
Crash reports are vital for software maintenance since they allow the developers to be informed of problems encountered in mobile application. Before fixing, need reproduce crash, which is an extremely time-consuming and tedious task. Existing studies conducted automatic crash reproduction with natural language described reproducing steps. Yet we find a non-neglectable portion only contain stack trace when occurs. Such stack-trace-only crashes merely reveal last GUI page occurs, lack...
Identifying and optimizing open participation is essential to the success of software development. Existing studies highlighted importance worker recommendation for crowdtesting tasks in order improve bug detection efficiency, i.e., detect more bugs with fewer workers. However, there are a couple limitations existing work. First, these mainly focus on one-time recommendations based expertise matching at beginning new task. Second, results suffer from severe popularity bias, highly...
Assurance cases (ACs) are structured arguments that allow verifying the correct implementation of created systems' non-functional requirements (e.g., safety, security). This allows for preventing system failure. The latter may result in catastrophic outcomes loss lives). ACs support certification systems compliance with industrial standards, e.g., DO-178C and ISO 26262. Identifying defeaters ---arguments challenge these --- is crucial enhancing ACs' robustness confidence. To automatically...
Large language models (LLMs) have significantly advanced the field of automated code generation. However, a notable research gap exists in evaluating social biases that may be present produced by LLMs. To solve this issue, we propose novel fairness framework, i.e., Solar, to assess and mitigate LLM-generated code. Specifically, Solar can automatically generate test cases for quantitatively uncovering auto-generated quantify severity generated code, develop dataset covers diverse set...
In recent years, the practice of fuzzing Deep Learning (DL) APIs has received significant attention in software engineering community. Many API-level DL fuzzers have been proposed to test individual by generating malformed input. Although these effective detecting bugs and outperforming prior work, there remains a gap bench-marking them against ground-truth, real-world libraries. Existing comparisons among primarily focus on detected but do not offer comprehensive, in-depth evaluation...
Crowdsourced software testing (short for crowdtesting) is a special type of crowdsourcing. It requires that crowdworkers master appropriate skill-sets and commit significant effort completing task. Abundant uncertainty may arise during crowdtesting process due to imperfect information between the task requester crowdworkers. For example, worker frequently chooses tasks in an ad hoc manner context, inappropriate selection lead worker's failing detect any bugs, unpaid wasted. Recent studies...
In this paper, we investigate the effectiveness of state-of-the-art LLM, i.e., GPT-4, with three different prompting engineering techniques (i.e., basic prompting, in-context learning, and task-specific prompting) against 18 fine-tuned LLMs on typical ASE tasks, code generation, summarization, translation. Our quantitative analysis these strategies suggests that prompt GPT-4 cannot necessarily significantly outperform fine-tuning smaller/older in all tasks. For comment best strategy prompt)...
Deep Learning (DL) libraries have significantly impacted various domains in computer science over the last decade. However, developers often face challenges when using DL APIs, as development paradigm of applications differs greatly from traditional software development. Existing studies on API misuse mainly focus software, leaving a gap understanding within APIs. To address this gap, we present first comprehensive study TensorFlow and PyTorch. Specifically, collected dataset 4,224 commits...
Large Language Models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in automatically generating code from provided natural language requirements. However, real-world practice, it is inevitable that the requirements written by users might be ambiguous or insufficient. Current LLMs will directly generate programs according to those unclear requirements, regardless of interactive clarification, which likely deviate original user intents. To bridge gap, we introduce a novel...
Deliberation is a common and natural behavior in human daily life. For example, when writing papers or articles, we usually first write drafts, then iteratively polish them until satisfied. In light of such cognitive process, propose DECOM, which multi-pass deliberation framework for automatic comment generation. DECOM consists multiple Models one Evaluation Model. Given code snippet, extract keywords from the retrieve similar fragment pre-defined corpus. Then, treat retrieved as initial...
Application Programming Interfaces (APIs) are designed to help developers build software more effectively. Recommending the right APIs for specific tasks is gaining increasing attention among researchers and developers. However, most of existing approaches mainly evaluated general programming using statically typed languages such as Java. Little known about their practical effectiveness usefulness machine learning (ML) with dynamically Python, whose paradigms fundamentally different from...
Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges due to the fundamental difference nature and construct general tasks, especially for developers who do not have backgrounds. Automatic code generation that produces snippet from natural language description can be promising technique accelerate tasks. In recent years, although many deep learning-based neural models proposed with high accuracy, fact most them...
Recently, many Deep Learning (DL) fuzzers have been proposed for API-level testing of DL libraries. However, they either perform unguided input generation (e.g., not considering the relationship between API arguments when generating inputs) or only support a limited set corner-case test inputs. Furthermore, developer APIs crucial library development remain untested, as are typically well documented and lack clear usage guidelines, unlike end-user APIs. This makes them more challenging target...
Automatic API recommendation can accelerate developers’ programming, and has been studied for years. There are two orthogonal lines of approaches this task, i.e., information retrieval-based (IR-based) sequence to (seq2seq) model based approaches. Although these were reported have remarkable performance, our observation finds major drawbacks, IR-based lack the consideration relations among recommended APIs, seq2seq models do not API’s semantic meaning. To alleviate above problems, we propose...
Checker bugs in Deep Learning (DL) libraries are critical yet not well-explored. These often concealed the input validation and error-checking code of DL can lead to silent failures, incorrect results, or unexpected program behavior applications. Despite their potential significantly impact reliability performance DL-enabled systems built with these libraries, checker have received limited attention. We present first comprehensive study two widely-used i.e., TensorFlow PyTorch. Initially, we...
The risk prediction in the process of bridge support maintenance and construction is very important to stability safety whole structure. Taking a project as case, this study analyzed sorted out factors from three aspects environment, management through expert investigation literature reading based on machine learning algorithm model, established index system, adopted grid search optimize hyperparameters model. model evaluation indexes R2 MAE were used evaluate results. results show that...