Data preparation for software vulnerability prediction: A systematic literature review
Software Vulnerability Prediction (SVP) is a data-driven technique for software quality
assurance that has recently gained considerable attention in the Software Engineering …
assurance that has recently gained considerable attention in the Software Engineering …
LineVD: statement-level vulnerability detection using graph neural networks
Current machine-learning based software vulnerability detection methods are primarily
conducted at the function-level. However, a key limitation of these methods is that they do …
conducted at the function-level. However, a key limitation of these methods is that they do …
Stacked ensemble model for optimized prediction of triangular side orifice discharge coefficient
MK Elshaarawy, AK Hamed - Engineering Optimization, 2024 - Taylor & Francis
This research focuses on optimizing the prediction of discharge coefficient (Cd) of triangular
side orifices (TSO) using a novel stacked model (SM) incorporating five machine learning …
side orifices (TSO) using a novel stacked model (SM) incorporating five machine learning …
Comparison between Adam, AdaMax and Adam W optimizers to implement a Weather Forecast based on Neural Networks for the Andean city of Quito
R Llugsi, S El Yacoubi, A Fontaine… - 2021 IEEE Fifth …, 2021 - ieeexplore.ieee.org
The main function of an optimizer is to determine in what measure to change the weights
and the learning rate of the neural network to reduce losses. One of the best known …
and the learning rate of the neural network to reduce losses. One of the best known …
Within-project defect prediction of infrastructure-as-code using product and process metrics
Infrastructure-as-code (IaC) is the DevOps practice enabling management and provisioning
of infrastructure through the definition of machine-readable files, hereinafter referred to as …
of infrastructure through the definition of machine-readable files, hereinafter referred to as …
Deepcva: Automated commit-level vulnerability assessment with deep multi-task learning
It is increasingly suggested to identify Software Vulnerabilities (SVs) in code commits to give
early warnings about potential security risks. However, there is a lack of effort to assess …
early warnings about potential security risks. However, there is a lack of effort to assess …
[HTML][HTML] VALIDATE: A deep dive into vulnerability prediction datasets
M Esposito, D Falessi - Information and Software Technology, 2024 - Elsevier
Context: Vulnerabilities are an essential issue today, as they cause economic damage to the
industry and endanger our daily life by threatening critical national security infrastructures …
industry and endanger our daily life by threatening critical national security infrastructures …
An empirical study of rule-based and learning-based approaches for static application security testing
Background: Static Application Security Testing (SAST) tools purport to assist developers in
detecting security issues in source code. These tools typically use rule-based approaches to …
detecting security issues in source code. These tools typically use rule-based approaches to …
On effort-aware metrics for defect prediction
Context Advances in defect prediction models, aka classifiers, have been validated via
accuracy metrics. Effort-aware metrics (EAMs) relate to benefits provided by a classifier in …
accuracy metrics. Effort-aware metrics (EAMs) relate to benefits provided by a classifier in …
Cross-project online just-in-time software defect prediction
S Tabassum, LL Minku, D Feng - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Cross-Project (CP) Just-In-Time Software Defect Prediction (JIT-SDP) makes use of CP data
to overcome the lack of data necessary to train well performing JIT-SDP classifiers at the …
to overcome the lack of data necessary to train well performing JIT-SDP classifiers at the …