Automated Logging Integration via NLP Defect Parsing
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Solution Overview
Problem
Current software development tools lack an efficient method for automated integration of log data generation into source code based on specific software defects, leading to inconsistent log formats, excessive extraneous data, and the need for manual trial and error in identifying relevant logging code placements.
Innovation Solution
A system utilizing natural language processing to parse historical defect reports, identify relevant source code, and generate logging rules that include defect type, placement, and format information, enabling automated integration of logging code into new source code for consistent and targeted logging output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual logging code insertion is used to aid defect analysis, then logging data can be generated to help analyze software defects, but the process requires additional analysis and multiple rounds of trial and error to determine where to insert logging code and what data to generate
Solution Approach 1:
The system enables self-service by automatically generating and inserting logging code without requiring developer intervention. The defect analysis system autonomously determines where logging code should be placed and what data should be captured, eliminating the manual trial-and-error process while preserving valuable logging data for defect analysis.
Solution Approach 2:
The system performs preliminary action by proactively inserting logging code at relevant locations in the source code before defect analysis begins. By anticipating where logging data would be most useful based on the defect description, the system eliminates the need for iterative trial-and-error insertion and immediately provides targeted logging output.
2Loss of information
If ad hoc logging code is inserted by developers to analyze defects, then specific log data can be generated, but the logging code lacks consistent format and content, and is often removed after defect resolution preventing future developers from leveraging previous experience
Solution Approach 1:
The system implements universality by creating a standardized logging framework that serves multiple purposes: it generates consistent formatted output for current defect analysis, preserves logging data for future reference, and adapts to different defect types while maintaining format consistency. This universal approach replaces ad hoc logging with a versatile system that benefits all developers analyzing defects.
Solution Approach 2:
The system incorporates feedback by analyzing the effectiveness of generated logging data and using this information to improve future logging code generation. The system learns from defect analysis patterns and adjusts logging placement and format to maximize information value, ensuring consistent and adaptable logging across different defects and developers.
3Productivity
If logging code is generated automatically at every function entry during normal operation, then logs are generated as part of normal operation, but the logged data includes a large amount of extraneous data that must be analyzed and filtered out and lacks specificity to particular software defects
Solution Approach 1:
The system applies extraction by selectively removing extraneous data from the logging output. Instead of logging all function entries indiscriminately, the system extracts and logs only the specific data relevant to the particular defect being analyzed, based on the defect description and type. This eliminates the need to filter through large amounts of irrelevant logging data while maintaining high automation.
Solution Approach 2:
The system implements local quality by tailoring the logging data to the specific local context of each defect. Rather than using a uniform logging approach for all functions, the system adjusts what data is logged based on the particular defect type and location, ensuring that each logging output contains precisely the relevant information needed for that specific defect analysis.
Data Source
AI summary
Using a natural language processing model, a historical defect report comprising a defect description in narrative text form is parsed. Within a code repository, source code associated with the historical defect report is identified. From the historical defect report and the source code, a logging rule comprising a defect type, logging placement information corresponding to the defect type, and logging format information corresponding to the defect type is generated. By parsing a new defect report using the natural language processing model, the new defect report reporting a defect in new source code, it is determined that the logging rule applies to the new defect report. Logging source code generating logging output when executed is placed within the new source code according to the logging rule. Execution of the new source code including the logging source code is caused, generating the logging output.