Script Dependency Branch Validation for Targeted Debugging
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Solution Overview
Problem
Software debugging is inefficient due to complex code dependencies, resource-intensive debugging processes, and difficulty in pinpointing errors across multiple code strings, especially in distributed systems, leading to increased computing resource utilization and delayed error resolution.
Innovation Solution
An integrated script development and validation platform that detects and records dependency branches in script development and script validation platforms based on detected dependency branches in script code, utilizing machine learning techniques to automate error detection and resolution of errors in real-time and in accordance with a preestablished hierarchy, reducing redundant evaluations and network communication overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive debugging is performed across all code strings to ensure thorough error detection, then error detection completeness is improved, but computing resource consumption increases
Solution Approach 1:
The patent segments the codebase into multiple code strings and establishes a dependency graph that identifies which code strings are relevant to each other. During debugging, only the code string containing the error and its dependent code strings are evaluated, rather than all code strings. This segmentation approach maintains error detection completeness for relevant code while avoiding unnecessary computation on unrelated code, thus resolving the contradiction between thorough error detection and computing resource consumption.
Solution Approach 2:
The patent performs preliminary analysis to build a dependency graph that maps relationships between code strings before debugging begins. This preliminary action pre-identifies which code strings are relevant to each other, enabling the debugging system to quickly determine the scope of evaluation without performing comprehensive checks during the actual debugging process. This preliminary structuring reduces computing resource consumption during error detection while maintaining completeness.
2Reliability
If iterative compilation cycles are performed to resolve interconnected errors across script sets, then error resolution completeness is improved, but productivity decreases
Solution Approach 1:
The patent segments the error resolution process by identifying and isolating the specific code string containing the error and its dependent code strings through dependency graph analysis. Instead of performing iterative compilation across all script sets, the system compiles only the relevant segmented portion. This segmentation maintains error resolution completeness for the affected area while significantly improving productivity by avoiding unnecessary compilation cycles in unrelated code.
Solution Approach 2:
The patent extracts the relevant code strings from the entire codebase by traversing the dependency graph starting from the code string containing the error. This extraction creates a minimized compilation scope that includes only the necessary code portions. By taking out only the relevant code for compilation, the system achieves complete error resolution for the affected area while dramatically reducing compilation time and improving overall productivity.
3Measurement precision
If comprehensive code evaluation is performed to accurately identify root causes, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent segments the code evaluation process by using the dependency graph to identify only the code strings that are relevant to the error at hand. The system evaluates the code string containing the error and its dependent code strings, while skipping unrelated code strings. This segmentation maintains root cause identification accuracy by ensuring all relevant code is examined, while reducing debugging time by avoiding evaluation of irrelevant code portions.
Solution Approach 2:
The patent introduces a dependency graph as an intermediary structure that pre-establishes relationships between code strings. This intermediary enables the debugging system to quickly determine which code strings require evaluation without performing comprehensive analysis during the debugging process. The dependency graph acts as a mediator that guides the evaluation process, ensuring measurement precision is maintained while significantly reducing the time required for root cause identification.
Data Source
AI summary
Systems and methods are described that allow for updating of software applications during testing of the application to detect errors as a result of execution of portions of the software application that prevent downstream portions of the software application from being evaluated. In an example, systems are described that are configured to detect errors during application execution. When an error is detected, the system obtains and executes specific script sets to debug the application. Based on the results of these debug operations, the system generates error reports that indicate issues within the software workflow. This ensures that errors in one part of the software do not hinder the evaluation of subsequent parts, allowing for a more efficient and thorough testing process.