ML Log Retrieval for Software Deployment Bottlenecks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Inefficient and subjective manual processes in software deployment hinder the identification of relations between software investigation issues, leading to delayed deployment of new or upgraded software products.
Innovation Solution
A machine learning system for automated similarity-based retrieval of software investigation log sets, which extracts features, generates representations, and uses statistical models to identify similar issues, thereby facilitating automated investigation and root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual activities are used to characterize software investigations, then technicians can perform investigations, but the process is inefficient and highly subjective leading to delayed deployment
Solution Approach 1:
The patent replaces manual mechanical characterization activities with automated machine learning-based text analysis. The system uses natural language processing and statistical models to automatically analyze investigation logs, extract features, and identify similar issues without human intervention, thereby eliminating the inefficiency and subjectivity of manual processes while accelerating deployment.
Solution Approach 2:
The system enables self-service by automatically analyzing investigation logs and identifying similar issues without requiring technician intervention for characterization. The machine learning models autonomously process the logs, extract relevant features, and retrieve similar cases from the knowledge base, allowing the system to serve itself rather than relying on manual technician efforts.
2Loss of information
If technicians separately characterize investigations using keywords of their choosing, then individual investigations can be documented, but important relations between issues investigated by different technicians may not become apparent
Solution Approach 1:
The patent transforms the characterization parameters from subjective technician-chosen keywords to objective, standardized features extracted by machine learning models. The system analyzes the full text content of investigation logs and extracts consistent features such as error types, component names, and failure patterns, ensuring that similar issues are identified regardless of which technician performed the investigation.
Solution Approach 2:
The patent creates a universal characterization system that processes all investigation logs through the same machine learning pipeline. The text analysis engine and statistical models provide a common framework for characterizing diverse investigation data, enabling the system to identify relations between issues across different technicians' work while eliminating the subjectivity of individual keyword choices.
3Productivity
If conventional manual practices are used, then software investigations can be performed, but the process unduly delays the deployment of new or upgraded software
Solution Approach 1:
The patent segments the deployment process into distinct automated stages: log collection, text analysis, feature extraction, similarity comparison, and result presentation. Each stage is handled by specialized machine learning components that process data independently and systematically, replacing the monolithic manual process with modular automated operations that reduce overall complexity and improve deployment efficiency.
Data Source
AI summary
A method in one embodiment comprises extracting features from each of a plurality of software investigation log sets, generating representations for respective ones of the software investigation log sets based at least in part on the corresponding extracted features, and storing the representations in a knowledge base. In conjunction with obtaining at least one additional software investigation log set, the method generates a representation of the additional software investigation log set, identifies one or more of the representations previously stored in the knowledge base that exhibit at least a specified similarity to the representation of the additional software investigation log set in accordance with one or more statistical models, and presents information characterizing the one or more software investigation log sets corresponding to respective ones of the identified one or more representations in a user interface. The method is illustratively implemented in a machine learning system of a processing platform.