Software fault prediction using data mining, machine learning and deep learning techniques: A systematic literature review
Software fault/defect prediction assists software developers to identify faulty constructs, such
as modules or classes, early in the software development life cycle. There are data mining …
as modules or classes, early in the software development life cycle. There are data mining …
Software defect prediction using artificial neural networks: A systematic literature review
The demand for automated online software systems is increasing day by day, which
triggered the need for high‐quality and maintainable softwares at lower cost. Software defect …
triggered the need for high‐quality and maintainable softwares at lower cost. Software defect …
[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 …
BPDET: An effective software bug prediction model using deep representation and ensemble learning techniques
SK Pandey, RB Mishra, AK Tripathi - Expert Systems with Applications, 2020 - Elsevier
In software fault prediction systems, there are many hindrances for detecting faulty modules,
such as missing values or samples, data redundancy, irrelevance features, and correlation …
such as missing values or samples, data redundancy, irrelevance features, and correlation …
SLDeep: Statement-level software defect prediction using deep-learning model on static code features
Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus
testing activities on more suspicious portions. Consequently, high-quality software is …
testing activities on more suspicious portions. Consequently, high-quality software is …
Software defect prediction for healthcare big data: an empirical evaluation of machine learning techniques
Software defect prediction (SDP) in the initial period of the software development life cycle
(SDLC) remains a critical and important assignment. SDP is essentially studied during few …
(SDLC) remains a critical and important assignment. SDP is essentially studied during few …
[HTML][HTML] Hybrid deep neural network with adaptive galactic swarm optimization for text extraction from scene images
D Pandey, BK Pandey, S Wairya - Soft Computing, 2021 - Springer
Text obtained in natural scenes contains various information; therefore, it is extensively used
in various applications to understand the image scenarios and also to retrieve the visual …
in various applications to understand the image scenarios and also to retrieve the visual …
[HTML][HTML] Software Defect Prediction Analysis Using Machine Learning Techniques
There is always a desire for defect-free software in order to maintain software quality for
customer satisfaction and to save testing expenses. As a result, we examined various known …
customer satisfaction and to save testing expenses. As a result, we examined various known …
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 …