Source Code Logging Analysis for Inconsistency Detection

Solution Overview

Problem

Insufficient and improper logging practices in software development lead to inconsistencies, such as varying log verbosity levels, reused logging statements without context matching, and unnecessary repetition, posing a security risk due to inadequate monitoring.

Innovation Solution

A system utilizing static code analysis and machine learning models, specifically BERT, Word2Vec, or sentence similarity transformers, to identify and classify logging inconsistencies in source code, providing notifications or auto-corrections to developers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If developers manually write and maintain logging statements, then logging coverage can be achieved, but logging inconsistencies occur due to lack of holistic view and clear guidelines

Engineering Contradiction:

Improvelogging consistencyVSAvoidlogging management complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system implements automated feedback by analyzing source code and identifying logging inconsistencies, then notifying developers of specific issues such as mismatched log levels or missing logging statements. This closed-loop feedback mechanism enables developers to correct inconsistencies without manual review of entire codebase, resolving the contradiction between maintaining logging consistency and managing logging complexity.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system enables self-service by automatically detecting and reporting logging inconsistencies without requiring developers to manually review logging practices. The automated analysis tool scans code, identifies issues, and provides actionable feedback, allowing developers to self-correct logging problems independently, thus improving consistency without increasing management complexity.

Inventive Principle:

Principle #25Self-service

2Reliability

If logging statements are added to monitor security-related events, then security monitoring is improved, but code complexity and maintenance burden increase

Engineering Contradiction:

Improvesecurity monitoring capabilityVSAvoidcode complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary action by automatically analyzing and identifying logging inconsistencies before they become production issues. By pre-detecting problems such as missing security event logging or inconsistent log levels, the system prevents complexity accumulation and ensures security monitoring requirements are met without manual intervention, resolving the contradiction between security monitoring capability and code complexity.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If developers focus on functionality, performance, and time-to-market, then product delivery speed is improved, but logging practices are neglected leading to inconsistencies

Engineering Contradiction:

Improveproduct delivery speedVSAvoidlogging quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system enables self-service by automatically detecting and reporting logging inconsistencies without requiring developers to manually review logging practices. The automated analysis tool scans code, identifies issues, and provides actionable feedback, allowing developers to self-correct logging problems independently, thus improving consistency without increasing management complexity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements automated feedback by analyzing source code and identifying logging inconsistencies, then notifying developers of specific issues such as mismatched log levels or missing logging statements. This closed-loop feedback mechanism enables developers to correct inconsistencies without manual review of entire codebase, resolving the contradiction between maintaining logging consistency and managing logging complexity.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12517805B2Automatic identification of logging inconsistencies in source code

Publication Date: 2026.01.06 SAP SE

AI summary

In an example embodiment, various types of logging inconsistencies contained in a source code repository are automatically detected and reported to the developers responsible for the maintenance of the software products. First, logging statements in the source code are identified, such as by using static code analysis tools (with some help if custom logging libraries or logging classes are used). For each logging statement, the function (e.g., the method) and/or the block of code in which the log resides is identified. Second, logging statements and their methods are analyzed pairwise in order to find the logging statements and the functions (or code blocks) that are similar to each other. In an example embodiment, this analysis is performed by a machine learning model, as will be described in more detail later.