Context-Based Error Resolution for Unknown CI/CD Pipeline Failures
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Solution Overview
Problem
Existing software development tools are limited in detecting and resolving unknown computational errors, particularly in complex CI/CD environments, leading to inefficient use of computational resources and poor user experience due to the lack of context-specific solutions.
Innovation Solution
A system that utilizes a machine learning model trained on context-based historically derived resolutions to known errors, offloading computationally heavy processes to remote servers, providing contextually accurate predictions of resolutions to unknown errors on client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing IDEs are used to detect errors locally on user devices, then users have full control over their software application and error detection is performed, but the amount of computer processing power and computer memory required increases significantly for complex software applications
Solution Approach 1:
The patent introduces a cloud-based error resolution system as an intermediary between the local IDE and the complex software application. The system collects error information from the IDE, processes it through remote machine learning models, and returns resolutions without requiring the local device to perform computationally intensive error analysis. This mediator approach allows accurate error detection while significantly reducing local computational resource consumption.
Solution Approach 2:
The patent replaces the mechanical system of local error compilation and analysis with a remote cloud-based system. Instead of the user device performing exhaustive compilation and error analysis locally, the system uses remote servers with machine learning models to analyze errors and provide resolutions. This substitution maintains error detection reliability while transferring the computational burden from local to remote resources.
2Ease of operation
If existing IDEs provide resolutions to known errors, then error resolution is assisted, but the resolutions are not keyed to particular context which may mislead or not resolve the underlying error
Solution Approach 1:
The patent applies local quality by analyzing the specific context in which each error occurs rather than providing generic resolutions. The system examines the surrounding code, the type of error, the platform, and the specific function where the error occurs to determine the most appropriate resolution. This context-aware approach ensures that each resolution is tailored to the specific situation, improving both ease of operation and resolution accuracy.
Solution Approach 2:
The system changes the parameters of error resolution by moving from static, pre-defined resolutions to dynamic, context-based resolutions. The machine learning model analyzes multiple parameters including error type, location, surrounding code structure, and platform information to generate customized resolutions. This parameter change enables the system to adapt resolutions to different contexts, preventing misleading generic solutions.
3Adaptability or versatility
If users manually research and recompile code to resolve unknown errors, then potential resolutions can be found, but the amount of computer memory, processing, and computer network traffic being utilized increases
Solution Approach 1:
The patent implements preliminary action by having the system pre-process and analyze errors through machine learning models before the user needs to resolve them. The system continuously learns from historical error data and pre-determines potential resolutions, so when an unknown error occurs, the answer is already prepared and can be immediately provided to the user without requiring manual research or repeated compilation attempts.
Solution Approach 2:
The system uses feedback mechanisms where users can confirm or correct proposed resolutions, and this feedback is fed back into the machine learning model to improve future predictions. This feedback loop allows the system to continuously improve its accuracy in predicting resolutions for unknown errors, reducing the need for users to manually research and recompile code repeatedly.
Data Source
AI summary
Reducing utilization of computational resources associated with providing predictions of resolutions to unknown computational errors via context-based historically derived resolutions to known errors may be facilitated. In some embodiments, update-information associated with an automation pipeline feature indicating an error may be received by a system. Based on the update-information, the system determines a context associated with the automation pipeline feature. The system may then determine that the error is associated with an update to a third-party software-development platform. The system may receive a set of messages from the third-party software-development platform indicating source code file-related information. The system may provide the context and the set of messages indicating source code file-related information to a machine learning model to generate a prediction indicating a resolution to the error. The system may then transmit the prediction indicating the resolution to the error to a software-module application installed on a remote device.