Continuous root cause analysis system for mobile apps via QA-SRE feedback loops

Abstract

System (100) for continuous root cause analysis of mobile applications via integrated QA-SRE feedback loops, wherein the system comprises the following: a data aggregation module configured to collect structured and unstructured data from test environments, production logs, telemetry, and user feedback; a correlation engine configured to analyze and link quality assurance (QA) test failures with production-level anomalies using pattern recognition and historical mapping; an anomaly detection module that can be operated to detect deviations in application performance using artificial intelligence or machine learning algorithms; a root cause analysis module configured to isolate defects by analyzing stack traces, log events, system resource usage, and device metadata; a feedback loop manager configured to facilitate automated bidirectional communication between QA teams and Site Reliability Engineering (SRE) teams; a recommendation engine configured to generate prioritized problem-solving suggestions based on historical bug data and code commit histories; a context-aware logging module configured to capture runtime environment metadata during both test and production execution; and a central orchestration dashboard coupled with a version control integrator and configured to visualize system insights and link problems to specific software builds or deployments; the system enables real-time detection, diagnosis, and resolution of application problems throughout the entire lifecycle of the mobile application.

Description

[0001] The present invention relates to the field of performance optimization and quality assurance of mobile applications. Specifically, it comprises a system and a method for continuous root cause analysis using integrated feedback loops between Quality Assurance (QA) and Site Reliability Engineering (SRE) teams. This facilitates the identification, diagnosis, and resolution of application problems in real time to improve stability, user experience, and deployment efficiency.

[0002] In the rapidly evolving field of mobile applications, ensuring consistent performance, reliability, and user satisfaction remains a constant challenge. Modern applications operate in complex environments with diverse devices, operating system versions, network conditions, and user behavior. Despite rigorous testing, many issues surface only after deployment, often due to insufficient sharing of context between Quality Assurance (QA) and Site Reliability Engineering (SRE) teams. Traditional error reporting and monitoring systems are not integrated, leading to delays in problem resolution and the recurrence of errors across different versions.

[0003] Currently, QA teams primarily focus on pre-release testing, while SRE teams address production issues. However, the lack of structured, real-time feedback loops between these two functions leads to fragmented insights. As a result, problems identified in production may not be efficiently traced back to their root cause, and QA environments may not reflect the real-world failure patterns observed by SRE. This disjointed process creates bottlenecks in root cause analysis and significantly impacts time to resolution and product quality.

[0004] Furthermore, the lack of automated correlation between test case failures and production anomalies makes it difficult to prioritize or reproduce errors. QA teams often work in isolation from operational metrics such as CPU utilization, crash frequency, memory leaks, or latency spikes observed in the live environment. Without continuous learning and adaptation processes based on live data, pre-release testing remains limited in scope, leading to regression and recurring errors in production.

[0005] The problem is exacerbated in agile development cycles, where frequent updates and rapid deployments leave little room for in-depth manual diagnostics. Existing solutions are reactive and do not support proactive problem detection or historical pattern recognition. Most systems lack a central intelligence mechanism capable of continuously collecting, analyzing, and acting on signals from the QA and SRE pipelines to drive systemic improvements.

[0006] Therefore, there is an urgent need for an intelligent system that establishes a closed feedback loop between QA and SRE and enables continuous root cause analysis throughout the entire software lifecycle. Such a system should bridge pre- and post-release environments, automatically capture test failures and correlate them with production incidents, learn from past patterns, and deliver actionable insights. This invention addresses these challenges by introducing a unified platform that increases application reliability, reduces downtime, and streamlines troubleshooting in modern mobile applications.

[0007] One objective of this disclosure is to enable the identification of root causes of problems in real time in QA and production environments.

[0008] Another objective of the present disclosure is to reduce problem-solving time through automated QA-SRE feedback loops.

[0009] Another objective of this disclosure is to improve the reliability of mobile applications and the satisfaction of end users.

[0010] Another objective of this disclosure is to improve cooperation and communication between QA and SRE teams.

[0011] Another objective of this disclosure is to provide intelligent, automated recommendations for recurring production problems.

[0012] Another objective of this disclosure is to support proactive and adaptive testing based on insights gained from live production.

[0013] Another objective of this disclosure is to provide centralized visibility and control via an integrated orchestration dashboard.

[0014] Another objective of this disclosure is to ensure the complete traceability of problems to specific builds, commits, and deployment phases.

[0015] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0016] The present invention relates generally to a system for continuous root cause analysis in mobile applications using intelligent QA-SRE feedback loops to improve problem detection, diagnosis and solution across development and production stages.

[0017] One embodiment of the present invention comprises a data aggregation module that collects structured and unstructured data from test environments, production logs, user feedback and telemetry for comprehensive analysis.

[0018] Another embodiment of the invention provides a correlation engine and an anomaly detection module that uses AI / ML to link test failures with production problems and to detect abnormal behavior patterns in real time.

[0019] Another embodiment of the invention is a root cause analysis module that isolates sources of error based on stack traces, performance metrics and context-related protocols and presents them via an interactive interface for faster troubleshooting.

[0020] Another embodiment of the invention is a feedback loop manager that automates bidirectional communication between QA and SRE, thus enabling adaptive testing and enhanced incident reporting.

[0021] Another embodiment of the invention is the integration of a recommendation engine and a version control mapper that provide actionable correction suggestions and trace problems back to specific builds to ensure continuous deployment with high reliability.

[0022] The present invention relates to a system for continuous root cause analysis in mobile applications through intelligent QA-SRE integration. It includes key modules such as the data aggregation module for collecting test and production data and the correlation engine for linking QA errors with live anomalies. The anomaly detection module and the root cause analysis module utilize AI to identify and diagnose critical performance issues. A feedback loop manager ensures continuous communication between QA and SRE, while the recommendation engine suggests corrective actions based on historical data. The system also includes a central dashboard and a version control integrator for traceability and real-time monitoring.

[0023] The invention is explained again below with reference to the figure. This shows: Fig. : a system (100) for continuous root cause analysis for mobile applications.

[0024] Fig.Figure 1 illustrates a system (100) for continuous root cause analysis for mobile applications. The invention provides a system (100) for continuous root cause analysis for mobile applications through integrated QA-SRE feedback loops, comprising several interconnected modules designed to operate seamlessly in pre-release and production environments. The system includes a data aggregation module that continuously collects structured and unstructured data from various sources, such as test execution logs, crash reports, user feedback, and telemetry from live environments. This data is fed into a correlation engine that intelligently links anomalies in production (e.g., crashes, latency spikes, memory leaks) with corresponding QA test case failures by using pattern recognition and historical analysis.An anomaly detection module uses AI / ML techniques to identify deviations in performance metrics and detect recurring error patterns across devices, operating system versions, or user segments. The Root Cause Analysis module processes these correlations to isolate the root causes by mapping stack traces, protocol signatures, and resource usage patterns, presenting them through an interactive visual interface. A feedback loop manager automates bidirectional communication between QA and SRE teams, enabling QA to adapt future test scenarios based on insights from live production, and providing SRE with enriched bug reports containing test context before release. Additionally, a recommendation engine delivers prioritized action items and automated fix suggestions based on historical solutions and code commit histories.All modules are orchestrated through a centralized orchestration dashboard that displays real-time KPIs, incident timelines, and quality gates to support continuous delivery pipelines. The system also includes a contextual logging module that captures environment-specific metadata during both test and production runs, improving traceability. A version control integrator links identified issues to specific builds, commits, and deployment logs, ensuring accountability and continuous improvement. Together, these modules form a robust, intelligent, and closed infrastructure for diagnosing, predicting, and resolving mobile application issues, significantly reducing turnaround times and improving product reliability.

Claims

[1] System (100) for continuous root cause analysis of mobile applications via integrated QA-SRE feedback loops, the system comprising: a data aggregation module configured to collect structured and unstructured data from test environments, production logs, telemetry, and user feedback; a correlation engine configured to analyze and link quality assurance (QA) test failures with production-level anomalies using pattern recognition and historical mapping; an anomaly detection module that can be operated to detect deviations in application performance using artificial intelligence or machine learning algorithms; a root cause analysis module configured to isolate defects by analyzing stack traces, log events, system resource usage, and device metadata; a feedback loop manager configured to facilitate automated bidirectional communication between QA teams and Site Reliability Engineering (SRE) teams; a recommendation engine configured to generate prioritized problem-solving suggestions based on historical bug data and code commit histories; a context-aware logging module configured to capture runtime environment metadata during both test and production execution; and a central orchestration dashboard coupled with a version control integrator and configured to visualize system insights and link problems to specific software builds or deployments; the system enables real-time detection, diagnosis, and resolution of application problems throughout the entire lifecycle of the mobile application. [2] System (100) according to claim 1, wherein the data aggregation module further integrates data from third-party monitoring tools, crash analysis platforms and device farms. [3] System (100) according to claim 1, wherein the correlation engine uses semantic log parsing and natural language processing (NLP) techniques to improve the accuracy of the error mapping. [4] System (100) according to claim 1, wherein the anomaly detection module continuously evaluates metrics such as memory usage, CPU load, network latency and crash frequency across multiple device types and operating system versions. [5] System (100) according to claim 1, wherein the root cause analysis module provides interactive visualizations of fault traces and performance metrics for faster troubleshooting. [6] System (100) according to claim 1, wherein the feedback loop manager generates automatic test case updates based on production errors identified by the SRE team. [7] System (100) according to claim 1, wherein the recommendation engine classifies corrective actions based on the severity of the impact, the frequency of occurrence and the historical solution time. [8] System (100) according to claim 1, wherein the context-related logging module automatically tags each log entry with details of the user session, network conditions and device parameters. [9] System (100) according to claim 1, wherein the central orchestration dashboard includes real-time alerts, key performance indicators (KPIs), trend analysis charts and root cause timelines.

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