MZ
Publications
- Learning to predict test effectiveness
- Format-aware learn&fuzz: deep test data generation for efficient fuzzing
- A comprehensive survey on non-invasive wearable bladder volume monitoring systems
- Learning to predict ssoftware testability
- An automated extract method refactoring approach to correct the long method code smell
- Format-aware Learn&Fuzz: Deep test data generation for efficient fuzzing
- Front Cover: International Journal of Intelligent Systems, Volume 37 Issue 8 August 2022
- Method name recommendation based on source code metrics
- A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms
- A systematic literature review on source code similarity measurement and clone detection: Techniques, applications, and challenges
- Estimating “depth of layer” (DOL) in ion-exchanged glasses using explainable machine learning
- Multi-type requirements traceability prediction by code data augmentation and fine-tuning MS-CodeBERT
- Testability-Driven Development: An Improvement to the TDD Efficiency
- Assessing neural markers of attention during exposure to construction noise using machine learning classification of electroencephalogram data
