Cognitive Script Builder for Automated JCL Error Correction
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Solution Overview
Problem
In dynamic environments, such as IBM mainframe systems, scripting languages like Job Control Language (JCL) face challenges due to human errors and system changes, leading to labor-intensive monitoring and potential system failures, which hinder the dynamic capabilities and require extensive updates.
Innovation Solution
The Cognitive Dynamic Script Building System employs a cognitive module that monitors system performance, identifies errors, and dynamically updates batch processes by using machine learning algorithms to analyze logs and system changes, determining solutions to errors and implementing them in the scripting language, thereby automating error correction and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and updating of scripting languages is performed, then system stability can be maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system enables self-service through automated error detection and correction mechanisms. The cognitive module continuously monitors script execution, automatically identifies errors in JCL scripts, determines appropriate solutions, and implements corrections without human intervention. This self-service approach maintains system stability while eliminating manual monitoring time consumption.
Solution Approach 2:
The system implements continuous feedback loops where execution results are automatically analyzed. When errors occur during script execution, the system receives feedback about the error type and context, processes this information through the cognitive module, and adjusts the script accordingly. This feedback mechanism ensures system stability is maintained through real-time corrections.
2Reliability
If manual error correction is performed, then system reliability can be maintained, but productivity decreases due to labor-intensive operations
Solution Approach 1:
The automated error correction system performs self-service by detecting and fixing errors without human intervention. The cognitive module analyzes error messages, determines appropriate corrections based on predefined rules and machine learning models, and automatically updates the scripts. This eliminates the need for manual error correction while maintaining system reliability and significantly improving productivity.
Solution Approach 2:
The system replaces manual mechanical operations with automated cognitive processes. Instead of human operators manually reviewing logs and correcting scripts, the cognitive module uses machine learning algorithms and automated reasoning to detect and fix errors. This substitution of mechanical human labor with automated intelligence maintains reliability while boosting productivity.
3Adaptability or versatility
If extensive updates to scripting languages are performed, then system adaptability can be improved, but device complexity and maintenance effort increase
Solution Approach 1:
The system handles adaptability requirements through self-service automation. When system changes require script updates, the cognitive module automatically detects the changes, determines necessary script modifications, and implements corrections without human intervention. This maintains system adaptability while reducing maintenance complexity by eliminating manual update efforts.
Solution Approach 2:
The system performs preliminary actions by proactively monitoring script execution and detecting potential errors before they cause system failures. The cognitive module continuously analyzes execution logs and identifies issues early, allowing corrections to be made before they impact system operation. This preliminary detection and correction approach improves adaptability while simplifying maintenance.
Data Source
AI summary
An embodiment of the invention may include a method, computer program product and computer system for updating a script of scripting language. The embodiment may include a computing device that receives an error message from the script operating in a run-time environment. The embodiment may include a computing device that determines a cause of the error message, wherein the cause of the error message is an erroneous segment of code of the script. The embodiment may include a computing device that determines a solution to the error message, wherein the solution to the error message is a replacement segment of code for the erroneous segment of code. The embodiment may include a computing device that creates a new script based on the replacement segment of code. The embodiment may include a computing device that operates the new script in the run-time environment.