Intelligent Fuzzing System for Remote Service Testing
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Solution Overview
Problem
Traditional code coverage tools and fuzzing techniques are inadequate for testing remote services, especially cloud services, as they require access to the underlying source code, which is often inaccessible, leading to impaired testing capabilities.
Innovation Solution
The system intelligently generates and modifies input data to exercise remote services, expanding error type response coverage by repeatedly sending API requests and analyzing responses to determine how the service handles invalid data, without requiring access to the source code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional code coverage tools are used to test remote services, then comprehensive code coverage can be achieved, but access to the service's source code is required which is often inaccessible in cloud environments
Solution Approach 1:
The patent introduces an intermediary testing system that sits between the client and the remote service. This intermediary captures and analyzes service responses without requiring access to the service's source code, enabling code coverage measurement through response analysis rather than direct code inspection. The system acts as a mediator that infers service behavior and coverage metrics from observable interactions.
Solution Approach 2:
The patent replaces the mechanical requirement of direct source code access with an automated response analysis system. Instead of mechanically requiring access to the service's internal code structure, the system substitutes this with automated analysis of service responses, error messages, and behavioral patterns to infer code coverage metrics.
2Reliability
If fuzzing techniques are applied to exercise remote services, then programming deficiencies can be identified, but the testing capability is impaired when source code is inaccessible
Solution Approach 1:
The patent implements a feedback-driven fuzzing system that analyzes service responses and error messages to adaptively generate subsequent test inputs. The system uses feedback from each interaction to refine its understanding of the service's behavior and to generate more effective fuzzing inputs that target unexplored code paths, enabling effective testing without source code access.
Solution Approach 2:
The testing system performs self-service by automatically generating fuzzing inputs, analyzing responses, and adapting its testing strategy without requiring external guidance from service documentation or source code. The system independently learns service behavior patterns and uses this knowledge to guide its own testing efforts, making the testing capability self-sufficient in cloud environments.
3Reliability
If repeated API requests are sent to expand error type response coverage, then service robustness can be determined, but testing time and resource consumption increase
Solution Approach 1:
The patent applies partial action by focusing fuzzing efforts on specific error types and response categories rather than attempting exhaustive testing of all possible service behaviors. The system identifies and targets particular error conditions that are most indicative of service robustness, performing sufficient testing to achieve reliable measurements without the need for exhaustive coverage of every possible code path.
Solution Approach 2:
The testing system employs periodic action by sending API requests in structured batches with systematic variation of parameters. Instead of continuous random testing, the system periodically introduces varied test inputs and systematically explores different error conditions, which improves testing efficiency by reducing redundant requests while maintaining comprehensive coverage.
Data Source
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AI summary
Improved techniques for testing an application to identify bugs. An API request body, which includes input data, is transmitted to a service to exercise the service. An error type response is received from the service, where the response indicates how the service handled the input data. The response is then used to determine an error type response coverage of the service. The coverage is then expanded by repeatedly performing a number of operations until a threshold metric is satisfied. For instance, in response to learning how previously-used input data impacted the coverage, new input data is generated. This new input data is designed to trigger new types of responses from the service. The new input is sent to the service, and a new error type response is received. These processes are repeated in an effort to expand the error type response coverage of the remote service.