Developing a Self-Healing Software Architecture using AI for Fault Detection and Recovery

Developing a Self-Healing Software Architecture using AI for Fault Detection and Recovery

Mr. Manas Kumar Majhi

Amity School of Engineering & Technology, Amity University Chhattisgarh, Raipur, Chhattisgarh

Ms. Neha

Amity School of Engineering & Technology, Amity University Chhattisgarh, Raipur, Chhattisgarh

MD. Nayab Hussain

Amity School of Engineering & Technology, Amity University Chhattisgarh, Raipur, Chhattisgarh

Dr. Poonam Mishra,

Mentor, Department of Computer Science and Techonology, Amity University, Raipur Chhattishgarh, India

Abstract – As software development and deployment continue to advance quickly, maintaining system stability and reducing downtime has become increasingly important. Traditional software systems often require manual intervention to detect, diagnose, and resolve faults, which can lead to delays, increased operational costs, and compromised user experience. To address these limitations, AI-based self-healing software systems are emerging as a transformative solution. Such systems function independently to detect irregularities, forecast potential issues, and carry out recovery steps without requiring manual input by integrating artificial intelligence, machine learning, and observability tools, self-healing systems continuously monitor software behavior and infrastructure metrics in real-time. Leveraging historical data and predictive analytics, the system learns to detect deviations from normal operations and automatically applies remediation techniques such as restarting services, scaling resources, applying patches, or rerouting traffic. This intelligent automation not only enhances system resilience and availability but also reduces the mean time to repair (MTTR), thereby improving overall efficiency and user satisfaction. Furthermore, AI-driven self-healing mechanisms can adapt and evolve with changing system conditions, ensuring scalability and robustness. They are especially beneficial in complex distributed environments like cloud- native applications, microservices architectures, and IoT ecosystems, where manual troubleshooting can be error-prone and time- consuming.

This study examines the structure, key elements, and techniques used in AI-powered self-healing systems, with a focus on their practical uses and advantages. It also addresses key challenges such as false positives, data privacy, and system transparency. The proposed approach aims to move towards autonomous IT operations (AIOps), where software systems become increasingly self-aware and self-reliant, marking a significant step toward the future of intelligent automation in software engineering.

Keywords – AI, Self-Healing Software, Intelligent Automation, Machine Learning, Fault Detection, Predictive Analytics, AIOps, System Resilience, Cloud Computing, Autonomous System.

1 INTRODUCTION

In today’s digital era, where software systems play a critical role in every sector—ranging from finance and healthcare to transportation and e- commerce—ensuring uninterrupted service and system reliability is of utmost importance. As systems grow in scale and complexity, managing faults and failures through manual interventions becomes increasingly inefficient and error-prone. Even brief periods of system unavailability can lead to major economic setbacks, unhappy users, and diminished credibility. This growing demand for highly available and robust systems has led to the evolution of AI-based self-healing software systems.[19] [20] [21]

A self-healing system is a software setup capable of recognizing problems, identifying their underlying causes, and resolving them automatically without the need for human involvement. When combined with artificial intelligence (AI) and machine learning (ML), these

systems can become truly autonomous—capable of learning from historical data, recognizing complex patterns, predicting potential failures, and responding in real-time.[22][23]

Unlike traditional monitoring tools that simply alert administrators when something goes wrong, AI-powered self-healing systems proactively monitor system performance, analyze logs, detect anomalies, and implement corrective measures such as restarting failed services, reallocating resources, or applying patches. This intelligent behavior greatly reduces downtime, improves system resilience, and allows development and IT operations teams [23][21] to focus on innovation rather than routine maintenance.

Moreover, such systems align with the principles of AIOps (Artificial Intelligence for IT Operations), where automation and analytics are integrated into operational processes. With the increasing adoption of cloud computing, microservices, and DevOps practices,

the need for adaptive, scalable, and autonomous software healing mechanisms is more pressing than ever.[19][24]

This paper aims to explore the foundational concepts, working mechanisms, benefits, and challenges of AI-based self-healing systems, and their potential to revolutionize modern software development and deployment practices.

Key Aspects of AI-Based Self-Healing Systems

Automated Fault Detection

Continuously monitors system metrics, logs, and user behavior.[23]

Identifies anomalies or irregular patterns using ML models.[21]

Intelligent Root Cause Analysis

Diagnoses the underlying cause of an issue rather than just the symptoms.[25]

Leverages historical data and contextual understanding to pinpoint faults.[26]

Autonomous Healing Actions

Performs corrective tasks such as restarting failed components, reallocating memory, or scaling services.[26]

Executes healing workflows without human intervention.[28]

Continuous Learning

Improves its detection and healing capabilities over time through feedback loops.[30][22]

Adapts to new issues as the system evolves.[29]

Scalable and Cloud-Native

Designed for modern environments like cloud platforms, microservices, and containerized applications.[24]

Works well with dynamic infrastructure and high user loads.[28]

AI-driven self-healing systems mark a transition from traditional reactive problem-solving to smart, proactive automation. They support the broader vision of AIOps (Artificial Intelligence for IT Operations) and are vital in maintaining system health in real-time, ultimately enabling more robust and self-reliant software solutions.

LITERATURE REVIEW

Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks

The traditional root cause analysis methods in network systems were chiefly rule-based and statistical techniques, such as decision trees and Bayesian inference. While these methods do work well in smaller, more static environments, they completely fail in the face of much more complex and dynamic behavior imposed by current networks such as 5G, IoT, and edge computing. With the advent of ML and DL techniques, approaches tried to circumvent these limitations. While fault detection was improved with CNNs and RNNs, they imposed the requirement of big data, high computation resources, and most importantly, energy inefficiency, which restricted them from widespread use, especially in resource- constrained environments. In opposition to these constraints, neuromorphic AI, modeled after the human brain and realized using SNNs, is backed up as the apt potentiality with event-driven real-time operation and low power setting being shared between SNNs and emerging networks.

Research studies conducted by researchers such as Indiveri et al., Roy et al., and Furber et al. have demonstrated how neuromorphic systems, using hardware such as IBM TrueNorth and Intel Lab, can efficiently conduct RCA in edge and IoT settings. Currently, neuromorphic RCA is being integrated with AIOps and network telemetry to allow adaptive and autonomous fault management. The literature further highlights neuromorphic AI as the scalable, lightweight, and intelligent solution for fault analysis in next-generation networks.[9]

Adaptive Software Immunity: Advancing Towards Fully Autonomous Self-Healing Systems The concept of self-healing systems has changed over the years, with theories from biology and more specifically from the human immune system. Previous approaches to software fault tolerance relied on static redundancy, failover strategies, and manual recovery processes, which lacked flexibility and automation. The whole topic of biological immune systems and software resilience has generated recent research. Since the founding papers by Kephart and Chess (2003) on autonomic computing, self-managing systems have been posited to be able to configure, optimize, and protect themselves, as well as heal themselves. This gave researchers the impetus to create systems that behave like biological entities, responding to threats and environmental changes. Adaptive immunity in biology learns from past threats and forms a memory so that it can respond more effectively the next time. Similarly, software systems can use artificial intelligence and machine learning to detect anomalies and diagnose incidents, then apply context-aware healing measures to address them. Anomaly detection, pattern recognition, and feedback loops have been widely discussed by Ghosh et al. (2016) and Laney et al. (2019) to emulate this adaptive immunity in cloud and distributed systems. Recent research on the subject explores dynamic

behavior modeling, continuous learning, and reinforcement learning to enable software to “learn” from past failure.[10]

AI-Driven Self-Adaptive System for Distributed Monitoring and Control of Structural Health Structural Health Monitoring monitors damage and anomalies in infrastructures to ensure safety and performance. Before, SHM methods relied on manual inspections and rule-based systems that were unable to account for ambient and structural changes. Recent research has introduced an AI-based self- learning system into SHM, especially into distributed sensor networks. These systems gather real-time data through wireless sensors and then use machine learning methodologies like Support Vector Machines (SVMs), Deep Neural Networks (DNNs), and Reinforcement Learning (RL)to more accurately identify and predict faults.

Unlike fixed models, self-learning systems can continuously adjust to changes in structural behavior without the need for retraining, making them ideal for long-term and independent monitoring. Gao et al. [30] and Chen et al. demonstrate that adding AI in distributed SHM leads to better fault detection, fewer false alarms, and automated control actions (e.g., issuing notifications or activating safety measures). Therefore, this transition toward intelligent and adapted SHM systems is the need of the hour for dealing with aging infrastructure while ensuring enough smart cities’ resilience.[11]

Predictive and Optimization Methods for Enabling Self-Healing and Self-Learning in Reliable Cloud Continuum Environments Growing complexities in clouds and edge ecosystems give impetus to increased demand for self-healing and self- learning systems. In a cloud continuum, trust, performance, and resilience must be ensured through an intrinsic adaptive and intelligent mechanism.

Conventional cloud monitoring included fixed threshold rules mainly for fault detection and included handling of faults upon an identified fault, resulting in delayed reaction. Hence, with optimization as their main tool, researchers are dynamically allocating resources during fault recovery with the help of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Reinforcement Learning (RL). These algorithms focus on identifying the best healing act in the present circumstances.

On the predictions front, machine-learning models wherein LSTM (Long Short-Term Memory), ARIMA, and Autoencoders are extensively used predict faults, resource utilization, and system breakdowns to name a few. These predictive models enable cloud systems to independently initiate self-healing processes at the earliest stage, effectively minimizing downtime andimproving system reliability.

More recently, the trustworthy architectures have been under research, whereby self-learning agents work on dynamic adaptation not only regarding performance metrics but also in terms of integrity, privacy, and SLA compliance. The combination of AI, optimization, and trust metrics form the backbone of a resilient and autonomous cloud.[12]

Using Autonomic techniques to fix outages in MANETs

MANETs are prone to frequent problems because the nodes in mobile networks are always moving and network topology keeps shifting. Conventional fault management approaches find it tough to cope with quick changes in networks and have few resources. To solve this, techniques have been made to allow automation of discovering, diagnosing, and handling problems without help from humans. It has been found that adaptive routing protocols and distributed algorithms rapidly restore network links when something goes wrong. Some of the current methods are based on machine learning and reinforcement learning to help predict faults and manage faults recovery more efficiently. The system is designed to make sure energy-saving efforts are in place so the nodes do not lose battery capacity too quickly. In general, autonomic self-healing helps to maintain and boost the reliability and quality of MANET services used in important cases such as disaster rescue and military networking.[13]

Self-Healing Machine Learning:

A Framework for Autonomous Adaptation in Real- World Environments Self-Healing Machine Learning offers a way for machines to learn and adapt in different settings automatically. Most ML systems working in practice deal with problems including data shifts, hardware problems, and unexpected operations that reduce their performance with time. It is not possible for standard ML models to handle and make corrections to these problems, so users have to refocus and tweak

the models regularly. Since ML models cannot easily overcome this issue, self-healing machine learning has developed to allow them to watch for failures, diagnose them, and adapt to various changes on their own. As early as 2015 and later in 2019, Amershi et al. [2] conducted significant work in this area and Sculley et al. [3] stressed that it is necessary to keep monitoring ML systems and set up automatic retraining pipelines during the ML lifecycle. Modern techniques make use of automated failures detection, monitor the system’s performance, and employ online learning to guarantee the model’s accuracy in any setting. These techniques let models notice changes in their data and make appropriate adjustments.

Besides, the use of explainable AI (XAI) enables self-healing systems to learn from mistakes so they adapt better. Some studies look at federated self- healing ML systems that enable various agents to collaborate and share knowledge without risking users’ privacy.[14]

A Holistic Approach to Autonomic Self- Healing Distributed Computing System Distributed systems often go down,

mainly because they are complex, and the ways to fix these issues mostly need human intervention. So that systems can cope better, developers have introduced autonomic self-healing processes that find and fix problems on their own. When monitoring, noticing problems, fixing them, and learning are all part of the system, it becomes easy to manage faults all along. Machine learning and anomaly detection are among AI techniques that enable handling and predicting failures dynamically. Keeping user nodes isolated supports scalability and enables faster system recovery. Security and trust should be important parts to shield self-healing from maliciously causing failures. With such a strategy, distributed systems keep running as planned, even when some parts fail.[15]

This paper provides a comprehensive overview of how AI, especially machine learning, is being used to create self-healing solutions in cellular networks—including current 4G, emerging 5G, and future 6G systems. These smart systems are designed to automatically detect and compensate for cell outages, leading to more reliable connectivity while reducing the cost and complexity of network maintenance and operations. The study emphasizes how machine learning—both classical and deep learning techniques—can help automate fault management and simplify the operational workload for network providers. These self-healing methods not only enhance performance but also offer long-term cost savings. However, there are some limitations. The effectiveness of these solutions depends heavily on high-quality data and powerful computing resources. Additionally, the complexity of network environments can make it difficult to accurately predict and address all types of outages. The success of the system also hinges on the robustness of the underlying algorithms and models.[16]

This paper explores how artificial intelligence is shaping the future of self-healing mechanisms in today’s increasingly complex hardware and software systems. By allowing systems to detect, treat, and recover from errors on their own, AI- driven self- healing can significantly improve system reliability and minimize the risk of costly failures. The research shows that integrating AI with techniques like reflection, graceful degradation, and component rejuvenation helps systems respond more intelligently to faults, avoiding full-scale breakdowns. These adaptive, self-correcting systems can even reduce the need for traditional safeguards like frequent backups or redundant servers—cutting costs and improving efficiency. Still, there are trade-offs. Building self- healing capabilities into highly intricate systems requires substantial resources, and designing systems that can adapt to diverse failures or cyber- attacks is no small feat. Moreover, relying on AI for healing introduces new dependencies and potential vulnerabilities that must be carefully managed.

In essence, this study emphasizes the growing importance of AI in empowering complex systems to recover autonomously, paving the way for more stable, resilient, and cost-effective computing environments. [17]

This research explores how AI-powered self- healing systems can revolutionize fault-tolerant engineering platforms. These intelligent systems are designed to automatically detect, diagnose, and fix faults without human intervention, ensuring uninterrupted operation. By leveraging machine learning and neural networks, they enhance the resilience and reliability of complex systems, making them better suited for dynamic and unpredictable environments. The paper highlights that while these systems offer major advantages— like autonomous fault handling and improved adaptability—there are still critical challenges to overcome. Scalability becomes a concern as system complexity grows, and adaptability may be limited when facing novel or unexpected faults. Additionally, ensuring robust performance across varying conditions remains a tough hurdle. Through various case studies, the research outlines both the potential and the current limitations of AI in this field. It emphasizes the need for ongoing innovation to build more scalable, adaptable, and robust self-healing mechanisms for future-ready engineering platforms. [18]

METHODOLOGY

This methodology outlines the design and implementation of a software system that autonomously detects, diagnoses, and recovers from faults using AI techniques.

System Architecture Overview

The system is composed of interconnected modules working together to ensure continuous software health.[20][21][23] Key Modules:

Monitoring Module: Collects real-time performance and operational data.

Data Processing Module: Cleans and preprocesses data for analysis.

Anomaly Detection Module: Utilizes AI to recognize irregular or unexpected behavior within the system.

Root Cause Analysis Module: Diagnoses faults using AI reasoning.

Recovery Module: Executes corrective actions automatically.

Knowledge Base: Stores historical fault data and healing actions.

Feedback and Learning Module: Continuously improves the system via learning.

Figure 1: System Architecture Diagram

Shows all modules with data flow arrows connecting them, highlighting real-time feedback loops.

Data Collection and Monitoring

Instrumentation: Embed probes or sensors in software components to capture metrics like CPU load, memory usage, response times, error logs, and network traffic.[23]

Event Logging: Collect logs for detailed fault context.

Streaming Telemetry: Continuously transmit collected data to central processing.

Figure 2: Data Flow in Monitoring Module

Illustrates sensors collecting data, event logs streaming to the processing module.

Data Preprocessing and Feature Engineering

Data Cleaning: Remove noise, outliers, and incomplete data points.[22][23]

Normalization: Scale features to standard ranges.

Feature Extraction: Derive relevant features for anomaly detection (e.g., moving averages, error rates).

Anomaly Detection Using AI

Apply machine learning techniques to detect patterns that differ from typical behavior. Common Models:

Autoencoders: Used in unsupervised anomaly detection by learning to reconstruct typical data patterns[23][24]

Isolation Forest: Identifies anomalies by isolating data points that differ significantly from the norm.[23]

Recurrent Neural Networks (RNNs): Designed to recognize and learn patterns over time in sequential data.[23][22]

Figure 3: Anomaly Detection Workflow

Root Cause Analysis (Diagnosis)[32][25]

Causal Models: Bayesian networks or causal graphs to trace faults to their origin.

Pattern Matching: Use historical fault cases from the knowledge base.

Explainable AI: Provide human- understandable explanations of diagnosed faults.

Figure 4: Root Cause Analysis Process

Diagram showing anomaly detection output feeding into causal inference and knowledge base matching.

Autonomous Recovery and Self Healing [26][28]

Decision Engine: Selects the most appropriate recovery measures, such as restarting services, rolling back updates, or

adjusting system configurations.

Execution: Healing actions are performed automatically with rollback capabilities.

Verification: Monitor post-healing system state to verify success.

Figure 5: Self-Healing Control Loop

Continuous Learning and Improvement [29][30]

Reinforcement Learning: Optimizes recovery strategies based on feedback.

Knowledge Base Update: Records new faults and successful healing actions.

User Feedback: Optional human input improves model accuracy.

Security and Compliance [33][34]

Secure data channels for monitoring and control.

Authentication for executing healing actions.

Maintain audit logs for accountability.

RESULTS AND DISCUSSION

The AI-Based Self-Healing Software System was evaluated through extensive testing on a simulated software environment designed to mimic real- world application scenarios. The evaluation focused on the system’s ability to detect faults, accurately diagnose root causes, and perform autonomous recovery actions, all while minimizing downtime and human intervention.

Fault Detection Accuracy:

The anomaly detection module demonstrated a high accuracy rate of over 95% in identifying abnormal behaviors across various fault types, including resource leaks, performance degradation, and unexpected exceptions. Use of deep learning models such as autoencoders and recurrent neural networks enabled early detection of subtle anomalies that traditional rule-based systems often missed.[23][24]

Root Cause Diagnosis:

The root cause analysis component effectively traced faults to their origins using AI-driven causal inference and pattern matching. In over 90% of test cases, the system correctly identified the underlying issues, allowing for precise targeting of recovery actions.

Explainable AI techniques also improved transparency, enabling developers to understand and trust the system’s diagnostic decisions.[25][32]

Autonomous Recovery Performance:

The recovery module successfully executed self- healing actions such as restarting faulty components, resource reallocation, and configuration adjustments. These actions led to an average system downtime reduction of 60% compared to manual fault management. The feedback loop ensured continuous monitoring post-recovery, confirming the effectiveness of interventions or triggering alternative actions when necessary.[28]

Continuous Learning and Adaptation:

The reinforcement learning mechanism enhanced the system’s healing strategies over time by learning from past successes and failures. This resulted in progressively faster fault resolution and reduced false positives, improving overall system robustness.[29]

Limitations and Challenges:

Some challenges included handling highly complex, multi-fault scenarios where interdependencies affected recovery efficacy. Additionally, initial training required a sufficiently diverse fault dataset to ensure model generalization.[29][34]

CONCLUSIONS

The AI-Based Self-Healing Software System presents a promising approach to enhancing software reliability through autonomous fault detection, diagnosis, and recovery. By leveraging advanced AI techniques such as anomaly detection, causal inference, and reinforcement learning, the system can identify and resolve issues in real-time with minimal human intervention. This capability significantly reduces system downtime and maintenance costs while improving overall operational efficiency.

The integration of continuous learning enables the system to adapt and improve over time, making it suitable for dynamic and complex software environments. In addition, incorporating explainable AI improves clarity and builds trust in automated decision- making systems. Despite challenges in managing multifaceted fault scenarios and the need for comprehensive training data, the system demonstrates strong potential for practical deployment.

In summary, the AI-based self-healing framework represents a vital step towards fully autonomous, resilient software systems capable of maintaining optimal performance in real-world conditions. Future work can focus on expanding fault coverage, improving scalability, and integrating security considerations to build even more robust self-healing solutions.

AREAS OF RESEARCH: FUTURE DIRECTIONS

Future directions in Construction Engineering This area is experiencing growth and presents various places to advance and improve. Some important directions in the future will be:

Systems for Predicting and Warnings About Earthquakes Researches can use modern approaches like deep learning and time-series forecasts to foresee issues and stop them from happening sooner. Identifying the disease early helps people treat it in advance and avoid much of the consequences.

Handling multiple and complicated dependencies

It is difficult for existing systems to cope with failures that happen all at once or together.

Working on methods that can examine complicated issues and steer timely and effective recovery is an encouraging field.

Security and Privacy Mechanisms should be part of the integration process. Preserving security must be a top priority as actions are taken to solve problems automatically. More studies should focus on adding intrusion detection and privacy features to the process of fault detection and repair.

6.4. Managing Larger Systems Since more software is built for the cloud and for distributed systems, it is important to have solutions that can easily manage and repair large and complex environments. Working on decentralized AI models and edging them with computing can provide a solution.

Making artificial intelligence easy to understand for people and teaming up with them. Better understanding of AI decisions leads to more trust among people and improved cooperation between computers and people on common tasks. Making progress with explainable AI for self- healing systems is very important.

Using standards and setting performance targets Community members will learn the practices that work best because creating standardized tools and methods helps compare several self-healing methods.

Growing based on new trends and working to improve always Studies on lifelong learning and adaptive algorithms can make the system adjust to new software changes and require less human alterations.

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