Results 71 to 80 of about 16,545 (157)

Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization

Algorithms
Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly.
Khadija Javed, Ren Shengbing, Muhammad Asim, Mudasir Ahmad Wani   +3 more
doaj   +1 more source

Data selection for cross-project defect prediction.

, 2019
Tiivistelmä Tausta: Tämä tutkimus edistää projektienvälisten virheiden ennustamisen nykytilan ymmärtämistä (CPDP) tutkimalla aihetta teemoissa, keskittyen erityisesti tiedollisiin lähestymistapoihin ja hakuperusteisen harjoitusdatan valintaan esittelemällä datan valintamenetelmiä ja tutkimalla niiden vaikutuksia.
openaire   +1 more source

ConCPDP: A Cross-Project Defect Prediction Method Integrating Contrastive Pretraining and Category Boundary Adjustment

IET Software
Software defect prediction (SDP) is a crucial phase preceding the launch of software products. Cross-project defect prediction (CPDP) is introduced for the anticipation of defects in novel projects lacking defect labels.
Hengjie Song, Yufei Pan, Feng Guo, Xue Zhang, Le Ma, Siyu Jiang   +5 more
doaj   +1 more source

A privacy-preserving federated meta-learning framework for cross-project defect prediction in software systems. [PDF]

Sci Rep
Potharlanka JL, Shaik KY, N BK.
europepmc   +1 more source

An explainable AI framework for enhanced software defect prediction using transformer-assisted boosting. [PDF]

Sci Rep
Kun Q, Shaikh ZA, Yang J, Kumar G, Mohammed HA, Yee PL.   +5 more
europepmc   +1 more source

Study on Residual Strength of Pipelines with Single-Point Uniform Corrosion Defects Under Internal Pressure Loading. [PDF]

Materials (Basel)
Chen L, Yu G, Liu D, Zhang Y, Zheng S, Wang X, Wang Y, Zhou L.   +7 more
europepmc   +1 more source

A taxonomy for detecting and preventing temporal data leakage in machine learning-based build prediction: A dual-platform empirical validation. [PDF]

PLoS One
Mishra LN, Rangari A, Nagrare S, Nayak SK.   +3 more
europepmc   +1 more source

Photon-counting detector CT virtual monoenergetic imaging for bone mineral density quantification: Validation with Micro-CT. [PDF]

iScience
Ma Y, Li Y, Su D, Du Y, Qi K, Wang S, Shen H, Ma M, Li F, Yang S, Miao Q, Yang X.   +11 more
europepmc   +1 more source

Effectiveness of machine learning for diagnosis and prognostic prediction of congenital heart disease: a systematic review and meta-analysis. [PDF]

BMC Pediatr
Wan W, Luo T, Zhang X, Guo C, Wang X.
europepmc   +1 more source

An integrated graph neural network model for joint software defect prediction and code quality assessment. [PDF]

Sci Rep
Dai P, Zhu H, Wu J, He H.
europepmc   +1 more source