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Errors in web-based applications negatively affect
user experience and cause operational efficiency losses for
businesses. This study proposes a hybrid error management
system integrated into CI/CD (Continuous Integration and
Continuous Deployment) pipelines. The system consists of two
main components: real-time monitoring of system metrics (API
response codes, CPU, memory, disk usage, etc.) using
Prometheus and Grafana, and analysis of user logs with ELK
Stack (Elasticsearch, Logstash, Kibana). The collected data was
cleaned in the preprocessing stage and balanced using the
SMOTE-ENN method. For error classification, a rule-based
model (if-else) was compared with machine learning algorithms
(SVM, KNN). The system was containerized with Docker and
Kubernetes and deployed via Jenkins, reducing error detection
time. This study provides a framework combining rule-based and
machine learning approaches to optimize traditional error
management processes.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Ahmet Albayrak
Berna Gövercin