Test Package Analyzer for Automated Test Case Modification
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Solution Overview
Problem
Current methods for identifying impacted test cases in software testing rely heavily on manual skills, which are time-consuming and error-prone due to the reliance on manual analysis of changes in software features.
Innovation Solution
A system and method that utilizes machine learning models to analyze release note packages, extract relevant keywords, and compare them with test package nomenclatures using pattern matching and AI techniques to recommend modifications to the test package automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis methods are used to identify impacted test cases, then human judgment and flexibility are maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computational system that uses natural language processing and pattern matching algorithms to analyze release notes and identify impacted test cases, eliminating human time investment while maintaining consistent accuracy
Solution Approach 2:
The system enables the test package to automatically identify its own impacted test cases by comparing release note keywords against test case nomenclatures, allowing the testing system to self-update without external human intervention
2Productivity
If manual skills are used for identifying impacted test cases, then flexibility in judgment is preserved, but the process becomes error-prone and inconsistent
Solution Approach 1:
The patent transforms the identification process from subjective human judgment to objective parameter-based matching by converting release notes and test case descriptions into comparable keyword parameters, ensuring consistent and reproducible results across different analyses
Solution Approach 2:
The system replaces variable human judgment with a deterministic computational algorithm that applies the same pattern matching rules consistently to all release notes, eliminating human error and inconsistency while maintaining high speed automated processing
3Productivity
If automated methods are implemented for test case identification, then time consumption is reduced, but the system complexity increases
Solution Approach 1:
The patent breaks down the complex analysis task into discrete segments: extracting keywords from release notes, storing test case nomenclatures, performing pattern matching comparisons, and generating modification recommendations. This modular segmentation reduces overall system complexity while maintaining high automated productivity
Solution Approach 2:
The system introduces an intermediary layer of keyword extraction and pattern matching that bridges the gap between unstructured release notes and structured test cases, simplifying the overall process by creating standardized intermediate representations that are easier to compare and analyze
Data Source
AI summary
A system and a method for recommending a modification to a test package for a software under test. A release note package associated to a feature of a software is received. The release note package is analysed in real time using machine learning based models. Further, a keyword is extracted from the release note package using a keyword extraction technique. The keyword corresponds to the feature of the software. The keyword is compared with nomenclatures present in a test package using a pattern matching technique. The test package is associated to the feature of the software. Finally, a modification to the test package is recommended based on the comparison. The modification comprises addition, deletion, or updating an existing element of the test package. It may he noted that the modification is recommended using an Artificial Intelligence (AI) technique.