Static Code Analysis Recommendation Service

Solution Overview

Problem

Software developers face challenges in selecting the most suitable static code analysis tool for their applications due to the numerous tools available, each with different features and requirements, leading to inefficiencies in resource usage and high costs associated with testing multiple tools.

Innovation Solution

A static code analysis recommendation service (SCAR) that utilizes machine learning techniques to intelligently select the most appropriate static code analysis tool and its configuration based on user preferences and codebase characteristics, optimizing analysis speed, resource usage, and accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If developers test multiple static code analysis tools manually, then they can evaluate tool performance thoroughly, but it consumes significant time and computing resources

Engineering Contradiction:

Improvetool evaluation accuracyVSAvoidtime for tool testing

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary automated evaluations of static code analysis tools on a diverse codebase before developers need to choose a tool. By pre-computing evaluation metrics (precision, recall, false positives, false negatives) for multiple tools in advance, the system eliminates the need for developers to manually test each tool, thereby reducing time loss while maintaining evaluation accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a virtual copy of the codebase and runs multiple static code analysis tools on this copy simultaneously in a controlled environment. This allows parallel evaluation of multiple tools without requiring developers to manually execute each tool separately, significantly reducing the time and resources needed for tool selection.

Inventive Principle:

Principle #26Copying

2Adaptability or versatility

If developers manually configure and test multiple static code analysis tools, then they can find the best tool for their specific needs, but it increases computing resource consumption and cost

Engineering Contradiction:

Improvetool selection suitabilityVSAvoidcomputing resource usage

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary evaluations of multiple static code analysis tools on a diverse codebase before developers need to choose a tool. By pre-computing evaluation metrics (precision, recall, false positives, false negatives) for multiple tools in advance, the system eliminates the need for developers to manually test each tool, thereby reducing time loss while maintaining evaluation accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a virtual copy of the codebase and runs multiple static code analysis tools on this copy simultaneously in a controlled environment. This allows parallel evaluation of multiple tools without requiring developers to manually execute each tool separately, significantly reducing the time and resources needed for tool selection.

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If the system evaluates multiple static code analysis tools with different configurations, then it can provide comprehensive recommendations, but it increases the complexity of the evaluation process

Engineering Contradiction:

Improveconfiguration optimizationVSAvoidevaluation process complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary evaluations of multiple static code analysis tools on a diverse codebase before developers need to choose a tool. By pre-computing evaluation metrics (precision, recall, false positives, false negatives) for multiple tools in advance, the system eliminates the need for developers to manually test each tool, thereby reducing time loss while maintaining evaluation accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system automatically varies evaluation parameters such as codebase size, language type, and tool configuration settings to create a comprehensive dataset. By systematically changing these parameters across multiple evaluation runs, the system generates robust recommendations that adapt to different developer needs without increasing manual intervention or process complexity.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11494285B1Static code analysis tool and configuration selection via codebase analysis

Publication Date: 2022.11.08 AMAZON TECH INC

AI summary

Techniques for static code analysis tool and configuration recommendation via codebase analysis are described. Multiple codebases are tested using multiple static analysis tools and corresponding configurations, and a machine learning model is trained based on the results and characteristics of the codebases. Users may provide a codebase to be analyzed and job preferences indicating what characteristics of static analysis they desire, the codebase may be analyzed to generate input data for the model, and the model may identify one or more similar testing runs. These candidate runs may be filtered and/or ordered based on the user's stated job preferences, and the resulting tools and configurations associated with these runs may be returned to the user or used to perform static analysis of the user's codebase.