AI Source Code Issue Localization Using Sentence Encodings
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Solution Overview
Problem
Complex software applications generated by AI often contain undetected issues such as bugs, unoptimized code, and malware, making it difficult to identify and fix these problems, leading to longer development cycles, unstable software, and security vulnerabilities.
Innovation Solution
An AI algorithm trained on a set of training sentence encodings of issues in source code is used to convert new identified issues into sentence encodings, which are then processed to identify the likely cause of the issue within the source code and suggest fixes, displayed to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI algorithms generate source code for complex software applications, then productivity is improved, but the difficulty of detecting and measuring issues increases
Solution Approach 1:
The patent introduces an intermediary AI issue identification system that acts as a mediator between the generated source code and human developers. This system automatically analyzes code, identifies potential issues, and presents them in a structured format, thereby resolving the contradiction by maintaining high productivity while reducing issue detection difficulty through an automated intermediate layer.
Solution Approach 2:
The patent replaces the mechanical manual code review process with an automated AI-based analysis system. Instead of relying on human developers to manually inspect complex generated code, the system uses machine learning models to automatically detect issues, substituting human effort with automated computational analysis that scales effectively with code complexity.
2Adaptability or versatility
If the size of software applications increases, then functionality is improved, but the ease of operation worsens
Solution Approach 1:
The patent extracts and isolates specific issues from the large codebase by using AI to identify and locate problems in their precise contextual locations. Rather than requiring developers to navigate entire large codebases, the system extracts relevant issue information including file locations, line numbers, and contextual code snippets, making issue fixing easier despite increased software size.
Solution Approach 2:
The patent segments the large software application into manageable analysis units by processing code in structured portions and presenting issues in discrete, location-specific formats. This segmentation allows developers to address individual problems without being overwhelmed by the overall system complexity, maintaining ease of operation despite increased functionality.
3Adaptability or versatility
If multiple software programs are used for identifying and correcting issues, then specialization is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple separate issue identification and correction tools into a single integrated AI-powered platform. Instead of requiring developers to switch between different specialized programs, the system combines code analysis, issue detection, localization, and fix recommendation capabilities into one unified interface, reducing overall system complexity while maintaining specialized functionality.
Solution Approach 2:
The patent creates a universal issue management system that performs multiple functions including identifying issues, locating them in source code, analyzing their context, and suggesting fixes. This multi-functional approach eliminates the need for multiple separate specialized programs, reducing device complexity while maintaining comprehensive issue analysis capability.
4Measurement precision
If manual issue identification and fixing processes are used, then precision is maintained, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by having the AI system pre-analyze generated code and identify potential issues before they reach human developers. The system proactively detects, locates, and prepares fix recommendations for issues, so that when developers review the code, the work is already partially completed, maintaining precision while significantly reducing the time developers spend on manual issue identification.
Data Source
AI summary
An AI algorithm is trained using a training set. The training set is a set of training sentence encodings of issues associated with different components of source code. For example, the set of training sentence encodings of issues may be floating point vectors. A new identified issue associated with a base of source code is received. Text associated with the new identified issue is converted into a set of one or more sentence encodings. The set of one or more sentence encodings are provided to the trained AI algorithm. In response to providing set of one or more sentence encodings to the trained AI algorithm, an output from the AI algorithm that identifies one or more files that are likely a cause of the new identified issue is received. The one or more files that are the likely cause of the new identified issue are displayed to a user.