Results 121 to 130 of about 277,827 (338)

Validating Unsupervised Machine Learning Techniques for Software Defect Prediction With Generic Metamorphic Testing

IEEE Access
In the realm of software defect prediction, unsupervised models often step in when labelled datasets are scarce, despite facing the challenge of validating models without prior knowledge of data.
Pak Yuen Patrick Chan, Jacky Keung
doaj   +1 more source

Risk of Retinopathy Associated with Long‐Term Use of Hydroxychloroquine in Patients with Rheumatic Diseases: A Systematic Review and Meta‐Analysis

Arthritis Care &Research, EarlyView.
Objective We aimed to estimate the prevalence and cumulative incidence of hydroxychloroquine retinopathy (HCQ‐R) and its risk factors among patients receiving long‐term HCQ with rheumatic diseases through a systematic review and meta‐analysis of observational studies that used spectral‐domain optical coherence tomography (SD‐OCT) for screening ...
Narsis Daftarian, Chloe Yue, Steve D Levasseur, Hui Xie, J. Antonio Avina‐Zubieta   +4 more
wiley   +1 more source

FORESIGHT: Software Defects Prediction from Requirements Change Requests Using Machine Learning Methods

Systems
Software defect prediction is becoming key for software quality assurance. Traditional software defect prediction approaches have predominantly focused on analyzing code-level metrics, often overlooking valuable information available during the ...
Hanan Helwa, Adel Taweel
doaj   +1 more source

Research and Appalication of Software Defect Predictionn based on BP-Migration learning

MATEC Web of Conferences, 2018
Software Defect Prediction has been an important part of Software engineering research since the 1970s. This technique is used to calculate and analyze the measurement and defect information of the historical software module to complete the defect ...
Zhang Jie, Wang Gang, Jiang Haobo, Zhao Fangzheng, Tian Guilin   +4 more
doaj   +1 more source

Software defect prediction using learning to rank approach. [PDF]

Sci Rep, 2023
Nassif AB, Talib MA, Azzeh M, Alzaabi S, Khanfar R, Kharsa R, Angelis L.   +6 more
europepmc   +1 more source

The Impact of Automated Parameter Optimization on Defect Prediction Models [PDF]

, 2018
Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto   +3 more
openalex   +1 more source

Thermoreflectance Detection of Point Defects Resulting from Focused Ion Beam Milling

Advanced Engineering Materials, EarlyView.
Focused ion beam (FIB) milling is a common tool for nanoscale material processing, however irradiation damage, redeposition, and contamination can occur. We use several characterization tools to show FIB‐induced effects beyond 1 mm from the milled area.
Thomas W. Pfeifer, Eric R. Hoglund, Anton V. Ievlev, Patrick E. Hopkins   +3 more
wiley   +1 more source

Machine Learning Approaches for Software Defect Prediction

Applied Computational Intelligence and Soft Computing
This paper analyses existing research about machine learning approaches in software defect prediction as a key element for improving software reliability and quality.
Hijab Zehra Zaidi, Ubaid Ullah, Muddassira Arshad, Hanan Aljuaid, Muhammad Arslan Rauf, Nadeem Sarwar, Rimsha Sajid   +6 more
doaj   +1 more source

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Foundations of Computing and Decision Sciences, 2018
Software defect prediction is a promising approach aiming to increase software quality and, as a result, development pace. Unfortunately, the cost effectiveness of software defect prediction in industrial settings is not eagerly shared by the pioneering ...
Hryszko Jaroslaw, Madeyski Lech
doaj   +1 more source

The Need for a Fine-grained approach in Just-in-Time Defect Prediction [PDF]

, 2021
Giuseppe Ng, Charibeth Cheng
openalex   +1 more source