Deep learning based vulnerability detection: Are we there yet?
Automated detection of software vulnerabilities is a fundamental problem in software
security. Existing program analysis techniques either suffer from high false positives or false …
security. Existing program analysis techniques either suffer from high false positives or false …
A systematic review on data scarcity problem in deep learning: solution and applications
Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …
applications. Deep learning models require a large amount of data to train the model. In …
Survey on software defect prediction techniques
Recent advancements in technology have emerged the requirements of hardware and
software applications. Along with this technical growth, software industries also have faced …
software applications. Along with this technical growth, software industries also have faced …
Analysis of transfer learning for deep neural network based plant classification models
Plant species classification is crucial for biodiversity protection and conservation. Manual
classification is time-consuming, expensive, and requires experienced experts who are often …
classification is time-consuming, expensive, and requires experienced experts who are often …
A survey of transfer learning
K Weiss, TM Khoshgoftaar, DD Wang - Journal of Big data, 2016 - Springer
Abstract Machine learning and data mining techniques have been used in numerous real-
world applications. An assumption of traditional machine learning methodologies is the …
world applications. An assumption of traditional machine learning methodologies is the …
Automatically learning semantic features for defect prediction
Software defect prediction, which predicts defective code regions, can help developers find
bugs and prioritize their testing efforts. To build accurate prediction models, previous studies …
bugs and prioritize their testing efforts. To build accurate prediction models, previous studies …
[HTML][HTML] On the use of deep learning in software defect prediction
Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
Deep semantic feature learning for software defect prediction
Software defect prediction, which predicts defective code regions, can assist developers in
finding bugs and prioritizing their testing efforts. Traditional defect prediction features often …
finding bugs and prioritizing their testing efforts. Traditional defect prediction features often …
A systematic literature review and meta-analysis on cross project defect prediction
Background: Cross project defect prediction (CPDP) recently gained considerable attention,
yet there are no systematic efforts to analyse existing empirical evidence. Objective: To …
yet there are no systematic efforts to analyse existing empirical evidence. Objective: To …
The impact of class rebalancing techniques on the performance and interpretation of defect prediction models
Defect models that are trained on class imbalanced datasets (ie, the proportion of defective
and clean modules is not equally represented) are highly susceptible to produce inaccurate …
and clean modules is not equally represented) are highly susceptible to produce inaccurate …