Finding the best learning to rank algorithms for effort-aware defect prediction
Abstract Context: Effort-Aware Defect Prediction (EADP) ranks software modules or changes
based on their predicted number of defects (ie, considering modules or changes as effort) or …
based on their predicted number of defects (ie, considering modules or changes as effort) or …
TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets
ALA Dalal, AM AlRassas, MAA Al-qaness, Z Cai… - Applied Energy, 2023 - Elsevier
Due to weather and political fluctuations that significantly impact the production and price of
energy sources, enhancing data distribution and reducing data complexity is crucial to …
energy sources, enhancing data distribution and reducing data complexity is crucial to …
A multi-agent reinforcement learning framework for optimizing financial trading strategies based on timesnet
Y Huang, C Zhou, K Cui, X Lu - Expert Systems with Applications, 2024 - Elsevier
An increasing number of studies have shown the effectiveness of using deep reinforcement
learning to learn profitable trading strategies from financial market data. However, a single …
learning to learn profitable trading strategies from financial market data. However, a single …
Machine learning techniques for stock price prediction and graphic signal recognition
Stock market analysis is extremely important for investors because knowing the future trend
and grasping the changing characteristics of stock prices will decrease the risk of investing …
and grasping the changing characteristics of stock prices will decrease the risk of investing …
The impact of feature selection techniques on effort‐aware defect prediction: An empirical study
Abstract Effort‐Aware Defect Prediction (EADP) methods sort software modules based on
the defect density and guide the testing team to inspect the modules with high defect density …
the defect density and guide the testing team to inspect the modules with high defect density …
Revisiting 'revisiting supervised methods for effort‐aware cross‐project defect prediction'
Effort‐aware cross‐project defect prediction (EACPDP), which uses cross‐project software
modules to build a model to rank within‐project software modules based on the defect …
modules to build a model to rank within‐project software modules based on the defect …
Diverse title generation for Stack Overflow posts with multiple-sampling-enhanced transformer
Stack Overflow is one of the most popular programming communities where developers can
seek help for their encountered problems. Nevertheless, if inexperienced developers fail to …
seek help for their encountered problems. Nevertheless, if inexperienced developers fail to …
Detecting multi-type self-admitted technical debt with generative adversarial network-based neural networks
Context: Developers often introduce the self-admitted technical debt (SATD), ie, a
compromised solution to satisfy the delivery of the current goals, in code comments but do …
compromised solution to satisfy the delivery of the current goals, in code comments but do …
Large language model ChatGPT versus small deep learning models for self‐admitted technical debt detection: Why not together?
J Li, L Li, J Liu, X Yu, X Liu… - Software: Practice and …, 2024 - Wiley Online Library
Given the increasing complexity and volume of Self‐Admitted Technical Debts (SATDs), how
to efficiently detect them becomes critical in software engineering practice for improving …
to efficiently detect them becomes critical in software engineering practice for improving …
A novel artificial neural network improves multivariate feature extraction in predicting correlated multivariate time series
P Eskandarian, JB Mohasefi, H Pirnejad… - Applied Soft …, 2022 - Elsevier
The existing multivariate time series prediction schemes are inefficient in extracting
intermediate features. This paper proposes an artificial neural network called Feature Path …
intermediate features. This paper proposes an artificial neural network called Feature Path …