DevOps Log Diff Analysis for Cross-Landscape Failure Recommendations
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Solution Overview
Problem
In current DevOps methodologies, issues identified in one software landscape are not effectively communicated to other landscapes, leading to potential failures in production environments due to differences in usage characteristics and resource constraints, resulting in lost time and reduced efficiency.
Innovation Solution
A system that extracts relevant data records from log messages, compares differences between pipeline runs, and uses a machine-learning model to identify failure-indicators and their solutions, enabling proactive identification and prevention of failures across software applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If issues and solutions are isolated to individual software landscapes, then each landscape can be managed independently, but failures occur in production environments due to lack of communication between landscapes
Solution Approach 1:
The patent combines failure information from multiple software landscapes into a unified machine learning model. The system ingests log messages, error codes, and failure patterns from diverse landscapes (development, testing, production) and integrates them into a shared knowledge base that enables cross-landscape failure prediction and prevention.
Solution Approach 2:
The machine learning model serves multiple functions: it detects failures in real-time, predicts potential failures before they occur, identifies root causes across different landscapes, and provides remediation recommendations. This universal system replaces multiple isolated monitoring systems with a single multi-functional platform.
2Measurement precision
If comprehensive log analysis is performed across all pipeline runs, then failure indicators can be identified accurately, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by training the machine learning model on historical log data from multiple pipeline runs before actual failure detection is needed. This pre-training enables the model to quickly identify failure indicators in real-time without performing comprehensive analysis during critical deployment windows.
Solution Approach 2:
The patent extracts only the most relevant features from log messages using Named Entity Recognition and other NLP techniques. Instead of analyzing entire log files, the system extracts key error codes, failure patterns, and critical parameters, significantly reducing processing time while maintaining detection accuracy.
3Measurement precision
If machine learning models are trained on extensive diff records from multiple landscapes, then failure prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer that standardizes and normalizes data from different software landscapes before feeding it to the machine learning model. This intermediary processing layer handles data transformation, feature extraction, and formatting, simplifying the overall system architecture while enabling the model to learn from diverse sources.
4Reliability
If proactive failure identification is implemented across all software applications, then production failures are reduced, but operational effort for data collection and analysis increases
Solution Approach 1:
The system implements self-service by automatically collecting log data from multiple landscapes, training the machine learning model, and generating failure predictions without requiring manual intervention. The automated pipeline continuously ingests new data, retrains models, and updates predictions, reducing operational effort while maintaining high reliability.
Data Source
AI summary
In an implementation of a computer-implemented method: to create extracted data records, an extract filter is instructed to extract relevant data records from log messages of two runs of a software pipeline. To create diff records using the extracted data records, a diff filter is instructed to compare and identify differences in messages between the two runs, where the diff records are amended with labeled data status information of a software pipeline run the extracted data records have been taken from. A recommendation engine is instructed to execute a machine-learning model training with the diff records. The recommendation engine is called to analyze the diff records for a failure-indicator. A determination is made that a failure causing the failure-indicator has been corrected in a later run of the software pipeline. A change is identified in a configuration or version of a software application associated with a correction. A failure-indicator-solution combination is generated.