Variable Value Identification in Software Testing via Jaccard Distance
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Solution Overview
Problem
Software development and testing environments face challenges in identifying incorrect variable values, which can lead to software bugs and defects, as manual analysis of large numbers of variables is burdensome and tedious using existing methods like program logs and debuggers.
Innovation Solution
A system and method that execute a test suite to record variable values as success or failure, calculate a confidence metric, and measure relevance using Jaccard distance between success and failure value sets to identify potentially incorrect variables, providing a filtered set for debugging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of variable values is performed using program logs and debuggers, then developers can identify software bugs, but the process becomes burdensome and tedious when dealing with large numbers of variables
Solution Approach 1:
The system automatically analyzes variable values by executing test suites and comparing success versus failure states without requiring manual developer intervention. The automated system serves itself by generating confidence metrics and relevance scores to identify potentially incorrect variable values, eliminating the burdensome manual analysis process while maintaining high identification accuracy.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. Instead of developers manually examining variable values in debuggers and logs, the system uses automated test execution, value comparison algorithms, and metric calculation to identify problematic variables, substituting human effort with machine-based analysis.
2Reliability
If all variable values are recorded and analyzed, then comprehensive bug identification is achieved, but the time and resources required increase significantly
Solution Approach 1:
The system applies different analysis depths and metrics to different variables based on their relevance scores. Instead of uniformly analyzing all variables with the same level of detail, the system prioritizes variables with higher relevance to test failures, applying more rigorous analysis only where needed. This localized quality approach ensures comprehensive bug identification in critical areas while reducing time spent on less relevant variables.
Solution Approach 2:
The patent introduces confidence metrics and relevance scores as parameters to prioritize analysis. By changing the parameter of analysis depth based on these metrics, the system dynamically adjusts how thoroughly each variable is examined. Variables with high relevance and low confidence values receive more intensive analysis, while others receive streamlined processing, optimizing the balance between reliability and time consumption.
3Productivity
If automated test suites are executed to record success and failure variable values, then identification of incorrect variables is improved, but the complexity of the testing system increases
Solution Approach 1:
The testing system is segmented into distinct functional modules: test suite execution, variable value recording, success/failure determination, confidence metric calculation, and relevance metric computation. Each module performs a specific function independently, making the overall complex system manageable through modular design. This segmentation allows the system to achieve high identification efficiency while keeping complexity localized to discrete, well-defined components.
Solution Approach 2:
The system employs universal data structures and algorithms that handle multiple types of variables and test scenarios through the same core logic. The variable value recording mechanism, confidence calculation, and relevance scoring are designed to work across different test cases and variable types uniformly, reducing the need for specialized handling code and managing system complexity through multi-functional design.
Data Source
AI summary
A test suite is executed to test a computer program for a plurality of variables. A variable value is generated for each variable of the plurality of variables at a predetermined location of the computer program. Whether a test included in the test suite is successful is determined. If the test is successful, the variable value is recorded as part of a success value set. If the test is not successful, the variable value is recorded as part of a failure value set. A confidence metric is calculated for the variable value. A relevance metric for each variable of the plurality of variables is measured, based on a Jaccard distance between the success value set and the failure value set. The failure value set, the success value set, the confidence metric for the variable value, and the relevance metric for each variable of the plurality of variables are presented.