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 …
Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation
Naïve Bayes (NB) is a well-known probabilistic classification algorithm. It is a simple but
efficient algorithm with a wide variety of real-world applications, ranging from product …
efficient algorithm with a wide variety of real-world applications, ranging from product …
Deep transfer learning approaches for Monkeypox disease diagnosis
Monkeypox has become a significant global challenge as the number of cases increases
daily. Those infected with the disease often display various skin symptoms and can spread …
daily. Those infected with the disease often display various skin symptoms and can spread …
Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16
While the world is still attempting to recover from the damage caused by the broad spread of
COVID-19, the Monkeypox virus poses a new threat of becoming a global pandemic …
COVID-19, the Monkeypox virus poses a new threat of becoming a global pandemic …
Software defect prediction via convolutional neural network
To improve software reliability, software defect prediction is utilized to assist developers in
finding potential bugs and allocating their testing efforts. Traditional defect prediction studies …
finding potential bugs and allocating their testing efforts. Traditional defect prediction studies …
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 …
Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning
Abstract Software Fault Prediction (SFP) is an important process to detect the faulty
components of the software to detect faulty classes or faulty modules early in the software …
components of the software to detect faulty classes or faulty modules early in the software …
[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 …
Handling class-imbalance with KNN (neighbourhood) under-sampling for software defect prediction
S Goyal - Artificial Intelligence Review, 2022 - Springer
Abstract Software Defect Prediction (SDP) is highly crucial task in software development
process to forecast about which modules are more prone to errors and faults before the …
process to forecast about which modules are more prone to errors and faults before the …
[LLIBRE][B] Feature engineering for machine learning and data analytics
Feature engineering plays a vital role in big data analytics. Machine learning and data
mining algorithms cannot work without data. Little can be achieved if there are few features …
mining algorithms cannot work without data. Little can be achieved if there are few features …