Autonomous, exception-driven code-fixing system with intelligent log analysis and automated merge requests

Abstract

An autonomous, exception-driven code-fixing system using intelligent log analysis and automatic merge requests, including: (a) a log collector and parser module configured to collect and analyze runtime logs from one or more software applications, extracting structured data including error messages, stack traces, and contextual metadata; (b) an exception classification and clustering engine configured to apply natural language processing and machine learning techniques to classify and cluster exception logs based on error type, frequency, severity, and contextual similarity; (c) a root cause analysis engine configured to analyze parsed logs and associated code artifacts, perform static code analysis, and control / data flow evaluation to locate error-prone areas of code corresponding to the exceptions; (d) a code fix generator configured to automatically generate code patches using rule-based systems and / or machine learning models trained on historical fixes, where the patches address the identified root cause and adhere to the coding standards of the target code base; (e) a patch validator configured to statically and dynamically test the generated patches using predefined or derived test cases in isolated environments and determine whether the patch fixes the exception without introducing new defects; (f) a merge request generator configured to automatically create and submit a merge or pull request to a version control system, the merge request containing the validated patch, metadata associated with the original exception, and test validation results; and (g) a feedback and learning module configured to collect the results of code reviews, the acceptance or rejection of patches, and system performance after deployment, and to retrain the classifier, root cause analysis, and code fix generation components based on feedback data.

Description

The present invention relates to a system for autonomous recognition, analysis and recovery of code exceptions in software systems using intelligent protocol analysis and automated merging requirements. The system integrates protocol monitoring, semantic exception analysis, cause detection, code synthesis, patch generation, and continuous integration pipelines to create self-healing software ecosystems. It reduces human intervention by creating merge requirements independently with proposed corrections for validation and provision.In modern software development, managing exceptions and recovering from errors in real time can be a complex and time consuming task. Conventional methods often involve manual interventions, which leads to delays in error recovery, longer downtime and a reduction in the overall efficiency of the system. Developers must search large protocols, reproduce problems, and manually determine the cause, resulting in longer development cycles and higher operating costs.The problem is exacerbated in large systems because the protocols may span multiple services, technologies, and platforms, creating a vast amount of data that is difficult to analyze effectively. Moreover, inconsistencies in the manner in which various faults are tracked, categorized, and resolved result in inefficiencies and missed opportunities for automation. Despite advances in monitoring tools and error tracking systems, manual steps are still required in detecting and cancelling exceptions, which hinders the potential for faster and automated solutions.To solve this problem, the invention introduces an autonomous, exception-driven code fixing system. This system is designed to automatically analyze protocols generated by various application components, detect patterns or anomalies that indicate exceptions or errors, and trigger smart actions to address these issues. Through the use of advanced machine learning models, the system can learn from historical data, predict potential fault points, and offer contextual solutions. This reduces the need for manual interventions, minimizes downtime, and accelerates the error recovery process.The invention also integrates automated merge requests to enable seamless, error-free code corrections. When a potential exception is detected, the system automatically generates a merge request to address the issue, including the required code changes, tests, and updates of the relevant configurations. In this way, it is ensured that the correction is applied in a consistent, comprehensible manner and can be quickly checked and provided, which improves the general system stability and performance.This autonomous approach greatly increases the efficiency of exception handling and code recovery, as the manual steps normally required are omitted. Thus, the development teams may focus more on innovations and new functions, rather than spend time with debugging. It also provides a scalable solution for large and complex systems where the amount of protocols and exceptions for manual management can quickly become too large.An object of the present disclosure is to reduce manual intervention by automatizing exception detection, analysis, and resolution.Another object of the present disclosure is to speed up software recovery by minimizing the mean time to recovery (MTTR) by real-time protocol analysis.Another object of the present disclosure is to improve code quality by generating contextual, error-specific patches that conform to the coding standards.Another object of the present disclosure is to increase system reliability by continually identifying and remedying exceptions before impacting the users.Another object of the present disclosure is to optimize development flows through the automatic generation and transmission of merge requests, thereby saving time in code reviews.Another object of the present disclosure is to increase productivity by automatizing repetitive debugging tasks and allowing developers to focus on developing new functions.Another object of the present disclosure is to enable proactive problem management by identifying recurrent faults and reducing the likelihood of their occurrence in the future.Another object of the present disclosure is to assist continuous learning through the use of feedback loops to refine models and to improve error recovery over time.Other 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.The present invention relates to the system that continuously collects runtime protocols from different applications or services and ensures that no errors are missed. It parses the protocols into structured formats and extracts substantial data such as error messages, time stamps and stack traces.Another embodiment of the present invention is the system that categorizes exceptions by type, severity, and frequency using natural language and machine learning models processing. It groups similar errors and helps prioritize problems with high impact.Another embodiment of the present invention is the root cause analysis module, which examines stack traces, and uses static code analysis to locate the exact faulty code regions. It constructs control and dataflow diagrams to identify the source of the exception with high certainty.In another embodiment of the present invention, after identifying the cause, the system automatically generates code patches using predefined rules or machine learning models trained on historical corrections. These patches remedy the root cause of the exception and conform to the project's encoding standards. This reduces the need for manual code debugging and speeds up the provision of corrections.Another embodiment of the present invention is that the generated code patches are tested in isolated environments, performing both static and dynamic tests. The patch validater ensures that the correction not only eliminates the problem, but also introduces no new errors.In another embodiment of the present invention, once the patch has passed validation, the system generates an automatic merge or pull request for integration into the version control system. The request contains detailed information such as the patch itself, protocols, test results, and references to the original exception.Another embodiment of the present invention is that the system is fully integrated into CI / CD pipelines to ensure that code changes smoothly transition from testing to production. The merge request automatically initiates a further validation within the deployment pipeline.Another embodiment of the present invention is that the feedback and learning module collects patch success data, including inspector comments and production performance. This information is used to retrain and refine the models of the system to make future corrections more accurate and efficient.The present invention relates to the autonomous exception driven code fixing system (100) via intelligent log analysis and automated merge requests, which automates the recognition, analysis and recovery of software exceptions. It consists of several key modules, including the log collector and parser module that collects and structures runtime protocols, and the exception classifier and clustering engine that categorizes errors for prioritization. The root cause analysis engine locates faulty code while the code fix generator creates smart patches. Finally, the merge request generator automates the delivery of patches, and the feedback and learning module improves the performance of the system by continuous learning. Together, these modules improve efficiency, reduce down time, and rationalize the software maintenance process.A further embodiment of the present invention consists in each module within the system playing a unique and coordinated role in order to ensure continuous automation from error detection to the transmission of code corrections.The Log Collector and Parser Module serves as a basic entry point into the system. It continuously collects protocols from various runtime environments, including distributed services, microservices architectures, or monolithic applications. This module supports both structured and unstructured protocol formats and is capable of interacting with widely used tools such as Fluentd or Logstash. After the protocols have been read in, they are broken down into structured data sets and important information such as time stamps, error messages, stack traces and context-related metadata is extracted for further analysis.Next, the Exception Classifier and Clustering module processes the structured protocols to recognize patterns and categorize exceptions. Using advanced natural language processing (NLP) models and machine learning algorithms, such as BERT or gradient boosting classifiers, this module classifies fault types and groups similar occurrences into clusters. It detects whether a fault is a recurrent problem or a new anomaly and provides it with relevant attributes such as severity, frequency and effects on the service. This classification reduces noise and allows the system to prioritize problems with high impact.Once the exception is classified, the Root-Cause Analysis (RCA) module locates the fault by examining the corresponding stack traces and performing static and dynamic code analysis. It constructs and traverses abstract syntax trees (ASTs), evaluates control and dataflow graphs, and maps execution paths to identify the exact code segment that is responsible for the error. This analysis is augmented by examining historical fixes and service dependencies, thereby assigning a confidence value to each identified suspect code block.The Code Fix Generator module is responsible for the autonomous synthesis of correcting code changes. It combines rule-based engines-for solving known errors such as null pointer exceptions or missing imports-with generative AI models, such as large speech models, that were trained using historical code patches. The module understands the semantic and syntactic context of the erroneous code and creates a patch that matches the style, architecture, and conventions of the existing codebase. This component is designed to be extensible and conformable so that it can handle various programming languages and frameworks.After the patch generation, the patch validater ensures the correctness and certainty of the proposed correction. It performs a series of static tests, including linting and type analysis, followed by dynamic tests in a sandbox environment. The validater performs predefined unit, integration and regression tests to confirm that the patch not only removes the identified exception, but also avoids introducing new errors. The module generates a validation report with test results and code coverage statistics that serves as the basis for the next phase.Once a patch has passed validation, the merge request generator automates the creation and transmission of merge or pull requests to version control systems such as GitHub, GitLab, or Bitbucket. Each merge request contains a detailed summary of the problem, the generated patch, diff comparisons, validation results, and links to the corresponding protocols and stack traces. The module can also be integrated into existing CI / CD pipelines and addresses enterprise policies by supporting multi-level code reviews, review tagging, and access control.Finally, the feedback and learning module provides a continuous improvement to the system by collecting the feedback from developers, code reviews, and merge results. It uses this data to retrain the models for exception classification, root cause analysis, and error recovery by applying techniques of enhanced learning and supervised learning. This module enhances the ability of the system to address similar problems in the future with greater precision and speed, thus enabling a self-developing autonomous framework for code maintenance.The invention is explained again below with reference to the figure. The following shows: FIG. 1 is an illustration of the autonomous, exception-driven code fixing system ( 100) using intelligent log analysis and automated merge requirementsFIG. 1 shows an illustration of the autonomous exception-driven code fixing system ( 100) using intelligent log analysis and automated merge requirements. The autonomous exception driven code fixing system operates by seamless integration of multiple smart modules that operate in a pipeline to detect, analyze, and remedy software exceptions in real time. The process begins with the log collector and parser module, which captures and analyzes runtime protocols from distributed application environments and thereby extracts important information such as error messages, stack traces, timestamps, and execution context. These structured protocols are then passed to the exception classifier and clustering module, which uses natural language processing and machine learning to categorize the exceptions, cluster similar fault events, and assign severity and frequency metrics. Once the errors are clustered, the root cause analysis (RCA) module analyzes the relevant code paths using stack traces, static code analysis, and control / dataflow evaluation to determine the most likely source of errors and create annotated suspect code regions with confidence values. This output is then used by the code fix generator, which synthesizes potential patches by either applying predefined correction rules or using AI-controlled models that understand the code semantics and context to suggest smart corrections. The generated patch is then checked by the patch verifier, which performs a rigorous validation process, including static analysis, test execution and regression testing in isolated environments, to ensure that the correction is safe and effective. After successful validation, the merge request generator automatically creates a merge or pull request containing the patch, metadata, and test results and transmits them to the version control systems, complying with the verification workflows and CI / CD protocols. Finally, all developers' feedback, test results, and merge status are fed into the feedback and learning module, which continuously refines the models and rules of the system and enables it to adapt and improve with each resolved exception.This end-to-end workflow, which is based on modular intelligence, allows the system to self-monitor, diagnose, repair, and provide corrections, thereby drastically reducing downtime and technical effort in servicing large software systems.

Claims

An autonomous exception driven code fixing system using intelligent protocol analysis and automatic merge requirements, comprising: (a) a protocol collector and parser module configured to collect and analyze runtime protocols from one or more software applications, extracting structured data including fault messages, stack traces, and contextual metadata; (b) an exception classification and clustering engine configured to apply natural language processing and machine learning techniques to classify and cluster exception protocols based on fault type, frequency, severity, and contextual similarity; (c) a root cause analysis engine configured to analyze parsed protocols and associated code artifacts, perform static code analyses and control / dataflow evaluations to locate error prone code regions corresponding to the exceptions; (d) a code fix generator configured to automatically generate code patches using rule-based systems and / or machine learning models trained on historical fixes, the patches corresponding to the identified root cause and meeting the programming standards of the target code base; (e) a patch verifier configured to statically and dynamically test the generated patches using predefined or derived test cases in isolated environments and determine whether the patch removes the exception without introducing new errors; (f) a merge request generator configured to automatically create and transmit a merge or pull request to a version control system, the merge request including the validated patch, metadata associated with the original exception, and test validation results; (g) a feedback and learning module configured to detect results of code checks, acceptance or rejection of patches, and system performance after deployment and retraining the classifier, root cause analysis, and components to generate code corrections based on feedback data.The system (100) of claim 1, wherein the protocol collector and parser module supports structured and unstructured protocols and interfaces to logging agents including Fluentd, Logstash, or proprietary protocol dispatchers.The system (100) of claim 1, wherein the exception classification and clustering module uses embeds generated by transformer-based language models to group semantically similar fault messages across different applications or services.The system (100) of claim 1, wherein the root cause analysis module performs abstract syntax tree (AST) traversal and code dependency graph analysis to identify suspect code locations with ranked confidence values.The system (100) of claim 1, wherein the code fix generator uses a large language model trained on source code repositories and fix-patterns to suggest contextual patches for novel exceptions.The system (100) of claim 1, wherein the patch validater runs test cases in containerized or virtualized sandbox environments and generates test coverage reports along with pass / fail metrics.The system (100) of claim 1, wherein the merge request generator marks relevant code reviews and integrates into continuous integration / continuous provision (CI / CD) pipelines for automated testing and staging.The system (100) of claim 1, wherein the feedback and learning module applies reinforcement learning to prioritize correction strategies that have given higher merge acceptance and production success rates in the past.

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