A literature review of using machine learning in software development life cycle stages
The software engineering community is rapidly adopting machine learning for transitioning
modern-day software towards highly intelligent and self-learning systems. However, the …
modern-day software towards highly intelligent and self-learning systems. However, the …
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 …
[PDF][PDF] Software defect prediction using supervised machine learning techniques: A systematic literature review
Software defect prediction (SDP) is the process of detecting defectprone software modules
before the testing stage. The testing stage in the software development life cycle is …
before the testing stage. The testing stage in the software development life cycle is …
[HTML][HTML] A survey on machine learning techniques applied to source code
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 …
The impact of software fault prediction in real-world application: an automated approach for software engineering
Software fault prediction and proneness has long been considered as a critical issue for the
tech industry and software professionals. In the traditional techniques, it requires previous …
tech industry and software professionals. In the traditional techniques, it requires previous …
Machine learning for software engineering: A systematic mapping
Context: The software development industry is rapidly adopting machine learning for
transitioning modern day software systems towards highly intelligent and self-learning …
transitioning modern day software systems towards highly intelligent and self-learning …
Predicting the Number of Software Faults using Deep Learning
W Alkaberi, F Assiri - Engineering, Technology & Applied Science …, 2024 - etasr.com
The software testing phase requires considerable time, effort, and cost, particularly when
there are many faults. Thus, developers focus on the evolution of Software Fault Prediction …
there are many faults. Thus, developers focus on the evolution of Software Fault Prediction …
[HTML][HTML] Evaluating the effectiveness of decomposed Halstead Metrics in software fault prediction
The occurrence of faults in software systems represents an inevitable predicament. Testing
is the most common means to detect such faults; however, exhaustive testing is not feasible …
is the most common means to detect such faults; however, exhaustive testing is not feasible …
Hybrid optimization-enabled deep Q network for fault prediction in service-oriented architecture
R Singh, K Kumar - The Journal of Supercomputing, 2024 - Springer
Fault prediction in service-oriented architecture-based models has been recognized as one
of the essential processes to reduce computational expenses and computational …
of the essential processes to reduce computational expenses and computational …
Improving prescriptive maintenance by incorporating post-prognostic information through chance constraints
Maintenance is one of the critical areas in operations in which a careful balance between
preventive costs and the effect of failures is required. Thanks to the increasing data …
preventive costs and the effect of failures is required. Thanks to the increasing data …