Network Test Automation Feasibility Scoring for Manual vs Automated Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining network test automation feasibility and maturity are resource-intensive, time-consuming, and prone to bias, lacking a standardized approach for deciding between manual and automated testing.
Innovation Solution
A method and system that calculates test automation maturity indices using a questionnaire with weighted questions, processed by AI models to assess ease, impact, coverage, and usefulness of automation, providing quantitative and qualitative measures for informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated testing is implemented, then testing speed and accuracy are improved, but resource consumption increases
Solution Approach 1:
The patent changes the parameters for decision-making by introducing feasibility indices and maturity indices with specific weightages. The feasibility index considers factors like test case complexity, automation effort, and resource requirements, while the maturity index evaluates current automation level and potential benefits. This parameter transformation enables quantitative comparison between manual and automated testing approaches, allowing organizations to select automation only when it provides net benefit, thus improving productivity while controlling resource consumption.
2Measurement precision
If manual assessment of automation feasibility is performed for every test case, then decision accuracy is improved, but time consumption increases
Solution Approach 1:
The patent segments the assessment process into two distinct indices: feasibility index for determining whether automation is worthwhile, and maturity index for evaluating current automation level. Each index has specific weighted criteria that can be assessed independently. This segmentation allows for more efficient evaluation by focusing on the most critical factors for each decision type, reducing the time required for comprehensive assessment while maintaining accuracy.
Solution Approach 2:
The patent transforms the manual assessment process into a parameter-based calculation system. By defining specific weightages for different factors (e.g., test case complexity, automation effort, resource requirements) and using standardized indices, the system converts subjective manual evaluation into objective quantitative measurement. This parameter transformation significantly reduces assessment time while improving decision accuracy through consistent, bias-free evaluation criteria.
3Reliability
If automated testing is implemented, then testing thoroughness is improved, but ease of operation deteriorates
Solution Approach 1:
The patent uses parameter changes by calculating feasibility indices that incorporate ease of automation as a weighted factor. The index considers the complexity of test cases, the effort required for automation scripting, and the availability of automation tools. By quantifying ease of operation as a parameter, the system can objectively determine when automation is feasible and when manual testing remains preferable, thus maintaining ease of operation while achieving thorough testing where automation is appropriate.
Data Source
AI summary
This disclosure relates to method and system for calculation of network test automation feasibility and maturity indices. The method includes receiving input data including user responses to a questionnaire associated with a test case. The questionnaire may include a first set of questions associated with a coverage of test case automation, and a second set of questions associated with a usefulness of test case automation. Each question of the questionnaire may include a corresponding weightage. The method further includes computing a coverage of automation index based on the user responses and a first set of weightages corresponding to the first set of questions, and a usefulness of automation index based on the user responses and a second set of weightages corresponding to the second set of questions.