Predicting the precise number of software defects: Are we there yet?
Abstract Context: Defect Number Prediction (DNP) models can offer more benefits than
classification-based defect prediction. Recently, many researchers proposed to employ …
classification-based defect prediction. Recently, many researchers proposed to employ …
[HTML][HTML] On the use of deep learning in software defect prediction
Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
A survey on machine learning techniques for source code analysis
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …
[HTML][HTML] A decade of intelligent software testing research: a bibliometric analysis
It gets harder and harder to guarantee the quality of software systems due to their increasing
complexity and fast development. Because it helps spot errors and gaps during the first …
complexity and fast development. Because it helps spot errors and gaps during the first …
Backpropagation Neural Network optimization and software defect estimation modelling using a hybrid Salp Swarm optimizer-based Simulated Annealing Algorithm
Abstract Software Defect Estimation (SDE) is a fundamental problem solving mechanism in
the field of software engineering (SE). SDE is a task that identifies software models that are …
the field of software engineering (SE). SDE is a task that identifies software models that are …
Multiple populations co-evolutionary particle swarm optimization for multi-objective cardinality constrained portfolio optimization problem
With the rapid development of financial market, a growing number of stocks become
available on the financial market. How to efficiently select these stocks to achieve higher …
available on the financial market. How to efficiently select these stocks to achieve higher …
[HTML][HTML] An empirical study on software defect prediction using codebert model
C Pan, M Lu, B Xu - Applied Sciences, 2021 - mdpi.com
Deep learning-based software defect prediction has been popular these days. Recently, the
publishing of the CodeBERT model has made it possible to perform many software …
publishing of the CodeBERT model has made it possible to perform many software …
Diversity based imbalance learning approach for software fault prediction using machine learning models
P Manchala, M Bisi - Applied Soft Computing, 2022 - Elsevier
The Software fault prediction (SFP) target is to distinguish between faulty and non-faulty
modules. The prediction model's performance is vulnerable to the class imbalance issue in …
modules. The prediction model's performance is vulnerable to the class imbalance issue in …
Dynamic behavior prediction of modules in crushing via FEA-DNN technique for durable battery-pack system design
The structural integrity and crashworthiness of the battery-pack system (BPS) in electric
vehicles are an emerging concern of engineers. Therefore, corresponding numerical and …
vehicles are an emerging concern of engineers. Therefore, corresponding numerical and …
Analysis and modeling conditional mutual dependency of metrics in software defect prediction using latent variables
NS Harzevili, SH Alizadeh - Neurocomputing, 2021 - Elsevier
Software defect prediction constitutes an important discipline in software development life-
cycle. Among the techniques employed in this domain, Naive Bayes (NB) classifier is cited …
cycle. Among the techniques employed in this domain, Naive Bayes (NB) classifier is cited …