Results 161 to 170 of about 198,133 (331)

Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques. [PDF]

J Healthc Eng, 2021
Khan B, Naseem R, Shah MA, Wakil K, Khan A, Uddin MI, Mahmoud M.   +6 more
europepmc   +1 more source

Smart AI-Intensive Software Defect Predictions Based on Optimized Deep Feature Classification Using GWO-Code2Vec CNN Learning

ITEGAM-JETIA
Software defect prediction plays a crucial role in improving software reliability, reducing maintenance costs, and enhancing software quality.
Ruckmani V. S, Rajan John, J. Jebamalar Tamilselvi, Surya Susan Thomas   +3 more
doaj   +1 more source

Multilabel classification for defect prediction in software engineering

Scientific Reports
With advancements in software development and artificial intelligence, defect prediction has gradually become an essential component of the software development lifecycle. Historically, defect prediction has been considered a multiclass classification problem because defect classes are mutually exclusive.
Jalaj Pachouly, Swati Ahirrao, Ketan Kotecha, Ambarish Kulkarni, Sultan Alfarhood   +4 more
openaire   +3 more sources

Software defect prediction framework based on hybrid metaheuristic optimization methods [PDF]

, 2015
A software defect is an error, failure, or fault in a software that produces an incorrect or unexpected result. Software defects are expensive in quality and cost.
Wahono, Romi Satria
core  

Predicting Defects in Software Using Grammar-Guided Genetic Programming

, 2008
The knowledge of the software quality can allow an organization to allocate the needed resources for the code maintenance. Maintaining the software is considered as a high cost factor for most organizations. Consequently, there is need to assess software
Dounias, Georgios, Tsakonas, Athanasios
core   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM). [PDF]

PeerJ Comput Sci, 2021
Farid AB, Fathy EM, Sharaf Eldin A, Abd-Elmegid LA.   +3 more
europepmc   +1 more source

incorporating qualitative and quantitative factors for software defect prediction

, 2012
Defect is an important quality attribute of software. Defect is injected in development process and depended on the maturity level of the processes. How many defects were detected is enough?
Chen Xichang, Wang Qing, Wang Dandan, Yang Ye, Zhang Liwen, Hong Zhenghua   +5 more
core  

Ferroelectricity in Antiferromagnetic Wurtzite Nitrides

Advanced Functional Materials, EarlyView.
We establish MnSiN2${\rm MnSiN}_2$ and MnGeN2${\rm MnGeN}_2$ as aristotypes of a new multiferroic wurtzite family that simultaneously exhibits ferroelectricity and antiferromagnetism with altermagnetic spin splitting. By strategically substituting alkaline‐earth metals, we predict new materials with coexisting switchable polarization, spin texture, and
Steven M. Baksa, Lin‐Ding Yuan, Stephen D. Wilson, James M. Rondinelli   +3 more
wiley   +1 more source

Active Learning for Software Defect Prediction

IEICE Transactions on Information and Systems, 2012
LUO, Guangchun, MA, Ying, QIN, Ke
openaire   +2 more sources