Hierarchical Clustering of Test Cases for IT Asset Testing
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Solution Overview
Problem
Current software development processes face challenges in efficiently selecting and optimizing test cases for information technology assets due to manual inefficiencies, duplication, and incomplete coverage, especially in complex systems with large numbers of test cases.
Innovation Solution
The implementation of a system that uses machine learning-based hierarchical clustering of test cases, leveraging testing logs to generate vector representations and identify clusters, thereby optimizing test case selection and generating effective testing plans for IT assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual test case selection and optimization is used, then human expertise can be applied to identify critical test cases, but the process becomes time-consuming and inefficient when dealing with large numbers of test cases
Solution Approach 1:
The patent replaces manual mechanical analysis of test cases with machine learning algorithms. The system automatically parses test execution logs, extracts feature information, generates vector representations, and performs hierarchical clustering to identify essential test cases, substituting human expertise with automated intelligent systems that scale efficiently to large test case volumes
Solution Approach 2:
The patent transforms test case data from unstructured log formats into structured vector representations by changing the parameter representation. Test cases are converted into numerical vectors based on parsed log events, enabling mathematical operations and clustering algorithms to efficiently identify patterns and select critical test cases without manual intervention
2Reliability
If comprehensive test case execution is performed on all test cases, then complete coverage is achieved, but resource consumption and execution time increase significantly
Solution Approach 1:
The patent extracts and identifies a subset of essential test cases from the complete test suite by analyzing execution logs and performing hierarchical clustering. This extraction process separates the most critical test cases that provide maximum coverage value, allowing the system to achieve comprehensive testing coverage by executing only the essential subset rather than all test cases
Solution Approach 2:
The patent applies partial action by executing only the essential test cases identified through clustering analysis rather than the complete test suite. The hierarchical clustering algorithm determines the optimal number of clusters and selects representative test cases from each cluster, achieving sufficient coverage with reduced execution volume
3Productivity
If test cases are clustered and grouped, then duplication is reduced and selection efficiency improves, but the complexity of the testing plan generation process increases
Solution Approach 1:
The patent segments the test case selection process into distinct automated stages: log parsing to extract feature information, vector representation generation to convert text to numerical data, hierarchical clustering to group similar test cases, and essential test case identification to select representatives. This segmentation transforms a complex manual process into manageable automated steps that reduce overall process complexity through systematic decomposition
4Productivity
If automated log parsing and vector representation is implemented, then test case analysis becomes faster and more scalable, but the initial system setup and processing overhead increase
Solution Approach 1:
The patent performs preliminary actions by pre-parsing test execution logs and pre-generating vector representations before the actual test case selection process. The system extracts feature information from logs in advance, creates structured vector data, and prepares the dataset for clustering. This preliminary processing transforms raw log data into ready-to-analyze vectors, significantly accelerating the subsequent selection process and making the system more scalable
Data Source
AI summary
An apparatus comprises a processing device configured to obtain testing logs generated by executing test cases on information technology assets of an information technology infrastructure, to parse the testing logs to generate a set of log event templates for testing actions performed during execution of the test cases on the information technology assets of the information technology infrastructure, to generate vector representations of the test cases utilizing the generated set of log event templates, and to perform, utilizing one or more machine learning-based hierarchical clustering algorithms that take as input the generated vector representations of the test cases, hierarchical clustering of the plurality of test cases. The processing device is also configured to generate, based at least in part on the hierarchical clustering results, testing plans for a given information technology asset of the information technology infrastructure.