Automated Failure Classification System for Software Testing
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Solution Overview
Problem
Current software testing methodologies lack automation for failure analysis, which is complex and requires significant computational resources and expertise, especially in fast software delivery cycles and diverse customer environments.
Innovation Solution
A system and method for failure classification and recommendation, utilizing a supervised learning approach to analyze failure patterns and provide automated recommendations for remediation, reducing the need for manual analysis and expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual failure analysis is performed by experts, then analysis accuracy is improved, but productivity deteriorates due to significant time and resource requirements
Solution Approach 1:
The patent introduces an automated failure analysis system as an intermediary between test execution and human expert analysis. This system uses machine learning models to pre-analyze test failures, extract relevant features, and generate initial diagnosis reports, thereby reducing the burden on human experts while maintaining analysis accuracy.
Solution Approach 2:
The system enables self-service failure analysis through automated test execution and result analysis. The machine learning models are trained on historical failure data to autonomously identify failure patterns, root causes, and remediation steps without requiring human intervention for each individual failure case.
2Productivity
If automated test execution is implemented, then productivity is improved, but device complexity worsens due to the need for sophisticated analysis systems
Solution Approach 1:
The patent segments the failure analysis system into modular components: test execution module, data collection module, machine learning model module, and result interpretation module. Each module handles specific tasks independently, reducing overall system complexity while enabling automated high-speed test execution and analysis.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational systems. Machine learning algorithms automatically process test results, identify failure patterns, and generate diagnoses, substituting human cognitive processes with computational mechanisms that scale efficiently.
3Adaptability or versatility
If diverse customer environments are supported, then adaptability is improved, but difficulty of detecting and measuring worsens due to varying failure patterns
Solution Approach 1:
The patent creates a universal failure analysis system that can handle multiple customer environments and software configurations. The machine learning models are trained on diverse historical data from various customers and scenarios, enabling the system to recognize and analyze different failure patterns across different environments using a single unified approach.
Solution Approach 2:
The system adapts to different customer environments by dynamically adjusting analysis parameters and thresholds based on the specific environment being tested. The machine learning models can retrain or fine-tune their parameters when exposed to new customer-specific failure patterns, maintaining effectiveness across diverse scenarios.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for implementing failure classification and recommendation delivery. An embodiment operates by receiving, by a failure classification and recommendation system, a plurality of conditions and patterns for a plurality of failures associated with one or more applications/functions automated executions using various tools and receiving one or more recommendations for each one of the plurality of failures. The embodiment further includes storing the plurality of conditions and patterns and the one or more recommendations for each one of the plurality of failures. This embodiment can further include receiving a report from a first tool indicating a failure associated with one or more applications/functions automated executions using the first tool and determining a recommendation based on the received report. A response is sent, by the failure classification and recommendation system, the first tool, where the response includes the recommendation to rectify the failure.