🤖 AI Summary
This study addresses the reliability challenges faced by modern web applications due to their inherent complexity and dynamic operating environments. The authors propose a modular self-healing framework grounded in the MAPE-K architecture, which innovatively integrates AutoFix-inspired heuristics with a learning-driven, feedback-guided recovery strategy to enable adaptive fault repair. Evaluated through fault injection experiments and iterative optimization in real-world scenarios, the system achieves an F1 score of 90.7% for fault detection and a 93.2% success rate in recovery, with an average recovery time of just 3.92 seconds. Notably, it sustains throughput at 88%–95% of baseline levels while increasing response time by only 3.1%, thereby significantly enhancing the resilience and autonomous recovery capabilities of web applications.
📝 Abstract
Ensuring the reliability and resilience of modern web applications remains a critical challenge due to increasing system complexity and dynamic runtime environments. This study proposes a modular self-healing framework based on the monitor-analyze-plan-execute over a shared knowledge base (MAPE-K) model, integrated with an AutoFix-inspired mechanism for adaptive fault recovery. Using a design and development research (DDR) approach, the system was implemented and evaluated through controlled fault injection experiments across twenty runtime failure scenarios, including service crashes, memory leaks, and database disconnections. Experimental results demonstrate that the proposed framework achieved a mean fault detection F1-score of 90.7% and a recovery success rate of 93.2%. The AutoFix module reduced the average time-to-recovery (TTR) by 56.2%, achieving an average recovery time of 3.92 seconds. System throughput was maintained between 88% and 95% during fault conditions, with only a 3.1% increase in response time. Additionally, iterative feedback mechanisms improved recovery efficiency by 18.6% over multiple cycles. These findings indicate that the proposed framework provides a practical and extensible approach to enhancing fault tolerance in web applications through feedback-driven adaptation. While the current implementation relies on predefined recovery strategies, the integration of learning-oriented feedback establishes a foundation for future development of more autonomous self-healing systems.