Dynamic Binary Data Collection for Testing Frameworks
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Solution Overview
Problem
Traditional test runners and frameworks are cumbersome and time-consuming, requiring developers to manually configure and parse extensive information during software testing, which can lead to inefficiencies and resource waste, as they often rely on static data collection methods that do not adapt dynamically to the binary being tested.
Innovation Solution
A testing framework that dynamically collects data by parsing binaries for specified characteristics, identifying additional characteristics, and collecting information before, during, and after execution, allowing for adaptive data collection based on the binary's features and test results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If static data collection methods are used in traditional test runners, then comprehensive system information can be collected, but the process becomes cumbersome and time-consuming with extensive manual configuration and parsing
Solution Approach 1:
The testing framework dynamically adapts data collection based on the binary being tested. It automatically identifies characteristics of the binary and selectively collects only relevant information, transforming the static, manual process into a dynamic, automated one that adjusts to each specific testing scenario
Solution Approach 2:
The system performs self-service by automatically analyzing the binary and determining what information needs to be collected. The framework autonomously configures itself without requiring manual setup, identifying relevant characteristics and collecting appropriate data independently
2Reliability
If extensive system information is collected statically, then complete testing data is obtained, but system resource usage increases
Solution Approach 1:
The framework applies local quality by collecting different types of information selectively based on the specific binary and testing needs. Rather than uniformly collecting all possible data, it tailors the data collection to the local requirements of each testing scenario, gathering only what is relevant
Solution Approach 2:
The system performs partial action by collecting only the necessary subset of information required for effective testing. It avoids excessive data collection by intelligently determining which characteristics and system information are actually needed, reducing resource consumption while maintaining testing reliability
3Ease of operation
If manual configuration is used for data collection, then precise control over collected information is achieved, but user effort and complexity increase
Solution Approach 1:
The framework enables self-service by automatically configuring itself based on the binary being tested. It performs self-analysis to determine what information to collect, eliminating the need for manual configuration while maintaining precise control over data collection
Solution Approach 2:
The system performs preliminary action by automatically analyzing the binary before testing begins and pre-configuring the data collection based on identified characteristics. This preliminary analysis and configuration eliminates the need for subsequent manual setup steps
Data Source
AI summary
Methods, systems, and computer program products are provided for dynamically collecting information corresponding to characteristics of a binary. A user or program inputs a path corresponding to a binary. A testing framework accesses a testing configuration that specifies one or more characteristics of a binary for which data collection is enabled. The testing configuration parses the binary to collect the characteristics. Based on the collected characteristics, the testing configuration identifies additional characteristics of the binary. The testing configuration collects information corresponding to identified additional characteristics of the binary.