A5053562000- Software Testing and Debugging Techniques
- Software Reliability and Analysis Research
- Software Engineering Research
- Software System Performance and Reliability
- VLSI and Analog Circuit Testing
- Machine Learning and Algorithms
- Advanced Malware Detection Techniques
- Web Application Security Vulnerabilities
- Topic Modeling
- Smart Agriculture and AI
- Electricity Theft Detection Techniques
- Machine Learning and Data Classification
- Robotic Path Planning Algorithms
- Network Security and Intrusion Detection
- Blockchain Technology Applications and Security
- Advanced Image and Video Retrieval Techniques
- Domain Adaptation and Few-Shot Learning
- Open Source Software Innovations
- Remote Sensing and Land Use
- Multi-Criteria Decision Making
- Robotics and Sensor-Based Localization
- Distributed Control Multi-Agent Systems
- Natural Language Processing Techniques
- Advanced Memory and Neural Computing
- Imbalanced Data Classification Techniques
Macau University of Science and Technology
2021-2025
University of Nottingham Ningbo China
2023
Nanjing University
2023
Jiangsu University
2013-2022
China National Heavy Duty Truck Group (China)
2022
Anhui Business College
2022
Goldwind (China)
2019
Huazhong University of Science and Technology
2012-2015
Hangzhou Normal University
2013
Random testing (RT) is a well-studied method that has been widely applied to the of many applications, including embedded software systems, SQL database and Android applications. Adaptive random (ART) aims enhance RT's failure-detection ability by more evenly spreading test cases over input domain. Since its introduction in 2001, there have contributions development ART, various approaches, implementations, assessment evaluation methods, This paper provides comprehensive survey on...
Testing web forms is an essential activity for ensuring the quality of applications. It typically involves evaluating interactions between users and forms. Automated test-case generation remains a challenge web-form testing: Due to complex, multi-level structure pages, it can be difficult automatically capture their inherent contextual information inclusion in tests. Large Language Models (LLMs) have shown great potential text generation. This motivated us explore how they could generate...
Random testing (RT), a fundamental software technique, has been widely used in practice. Adaptive random (ART), an enhancement of RT, performs better than original RT terms fault detection capability. However, not much work done on effectiveness analysis ART the combinatorial test spaces. In this paper, we propose novel family ART-based algorithms for generating suites, mainly based fixed-size-candidate-set and restricted (that is, by exclusion). We use empirical approach to compare sets...
Random testing (RT) has been identified as one of the most popular techniques, due to its simplicity and ease automation. Adaptive random (ART) proposed an enhancement RT, improving fault-detection effectiveness by evenly spreading test inputs across input domain. To achieve even spreading, ART makes use distance measurements between consecutive inputs. However, nature object-oriented software (OOS), measurement can be particularly challenging: Each may involve multiple classes, interaction...
Unit testing validates the correctness of units software system under test and serves as cornerstone in improving quality reliability. To reduce manual efforts writing unit tests, some techniques have been proposed to generate assertions automatically, including deep learning (DL)-based, retrieval-based, integration-based ones. Among them, recent approaches inherit from both DL-based retrieval-based are considered state-of-the-art. Despite being promising, such suffer inherent limitations,...
Adaptive random testing (ART) aims at enhancing the effectiveness of (RT) by more evenly spreading test cases over input domain. Many ART methods have been proposed, based on various, different notions. For example, distance-based (DART) makes use concept distance to implement ART, attempting generate new that are far away from previously executed ones. The Euclidean has a popular choice metric, used in DART evaluate differences between cases. However, is most suitable for DART? To answer...
Artificial intelligence (AI) has revolutionized software engineering (SE) by enhancing development efficiency. The advent of pre-trained models (PTMs) leveraging transfer learning significantly advanced AI for SE. However, existing PTMs that operate on individual code tokens suffer from several limitations: They are costly to train and fine-tune; they rely heavily labeled data fine-tuning task-specific datasets.In this paper, we present TransformCode, a novel framework learns embeddings in...
Combinatorial interaction testing is a well-recognized method, and has been widely applied in practice, often with the assumption that all test cases combinatorial suite have same fault detection capability. However, when resources are limited, an alternative may be some more likely to reveal failure, thus making order of executing critical. To improve cost-effectiveness, prioritization employed. The most popular approach based on coverage, which prioritizes by repeatedly choosing unexecuted...