Function Call Graph Search for Software Troubleshooting
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Solution Overview
Problem
Current knowledge base systems for software troubleshooting are manual, time-consuming, and prone to producing low-quality data due to lack of incentives for analysts, and keyword-based searches often fail to return relevant results.
Innovation Solution
Automating the process of recording and searching problem determination data by converting trace program output into function call graphs (FCGs), which are stored and used to improve the quality and consistency of search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual recording and searching of problem determination data is used, then flexibility and adaptability are maintained, but time consumption and productivity are significantly reduced
Solution Approach 1:
The system automatically generates function call graphs from trace data and performs pattern matching without human intervention. The automated knowledge base population process allows the system to serve itself by extracting problem determination data, structuring it into FCGs, and storing them for future queries, eliminating the need for manual data entry while maintaining operational flexibility through configurable patterns.
Solution Approach 2:
The patent replaces manual mechanical processes (analysts manually recording data and performing keyword searches) with automated computational processes. The system uses automated trace data parsing, FCG generation algorithms, and pattern matching engines to substitute human analysts' manual work, dramatically improving productivity while maintaining the ability to handle complex problem determination scenarios.
2Manufacturing precision
If skilled analysts manually input problem determination data, then data quality may be maintained through expertise, but time consumption increases and entry-level analysts cannot contribute
Solution Approach 1:
The system automatically extracts problem determination data from trace files, generates FCGs, and populates the knowledge base without requiring analyst intervention for data entry. This self-service approach maintains data quality through automated extraction and validation processes while eliminating time-consuming manual input and enabling any analyst to contribute to the knowledge base.
Solution Approach 2:
The patent replaces manual data entry processes with automated computational extraction and transformation of trace data into structured FCGs. The automated system maintains data quality through consistent application of extraction rules and validation logic, eliminating variability and time consumption associated with manual entry by less skilled analysts.
3Ease of operation
If keyword-based search is used in knowledge base, then simple queries are fast and easy, but search relevance and measurement precision are poor
Solution Approach 1:
The patent transforms the search parameter from simple keywords to structured function call graphs with defined patterns. By changing the search parameter from unstructured text to structured graphical representations with configurable patterns, the system maintains ease of operation through pattern-based queries while dramatically improving search result relevancy through precise pattern matching against stored FCGs.
Solution Approach 2:
The patent introduces FCGs as an intermediary representation between the raw trace data and the search query. The FCG serves as a structured intermediary that captures the essential problem determination information in a searchable format, allowing analysts to query using pattern descriptions while the system performs precise structural matching, thereby improving relevancy without sacrificing simplicity.
4Quantity of substance
If conventional knowledge bases store data in searchable database records, then data storage is efficient, but data quality and consistency cannot be guaranteed
Solution Approach 1:
The patent changes the data structure parameter from unstructured or semi-structured database records to structured function call graphs with defined schemas and patterns. This structural transformation enables the system to maintain data quality consistency through enforced pattern structures while preserving efficient storage capabilities through the graphical representation and indexing mechanisms.
Solution Approach 2:
The patent applies different quality control measures to different parts of the stored data. The FCG structure enforces specific quality requirements on problem description, cause, and resolution fields locally, ensuring that each component meets its specific quality standards while allowing the overall system to store diverse problem types efficiently with appropriate local validation rules.
Data Source
AI summary
A trace file providing details of an execution of a software application experiencing unexpected behavior can be identified. The trace file can be converted into a graph structure (e.g., Function Call Graph), which details functions called during the execution, a calling relationship, and errors encountered during the execution. The converted graph structure can be programmatically matched against a set of stored graph structures to determine matched results. Each stored graph structure can correspond to unique record of a symptom database. Each unique record can be associated with a determined problem. The matched results can be provided as possible problems causing the unexpected behavior of the software application.