Predictive Software Test Case Scheduling Across IT Assets
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Solution Overview
Problem
Conventional methods for scheduling software application test case execution on IT assets are inefficient and resource-intensive, often requiring manual processes that are time-consuming and prone to errors.
Innovation Solution
An automated scheduling system that utilizes machine learning to predict test case execution times based on historical data, generating optimized schedules to minimize total completion time and improve resource utilization by analyzing execution histories and employing linear regression analysis to populate missing execution time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual scheduling techniques are used for test case execution, then human control and flexibility are maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables automated self-scheduling of test cases by using historical execution time data to generate optimized schedules without human intervention. The scheduler automatically processes test case requirements, predicts execution times, and assigns test cases to IT assets, eliminating manual scheduling efforts while maintaining operational control through automated decision-making
Solution Approach 2:
The patent replaces manual mechanical scheduling processes with an automated computational system that uses historical data analysis and prediction algorithms. The mechanical act of manual schedule creation is substituted with an automated scheduling engine that processes execution time data and generates optimized schedules, significantly reducing time consumption and human error
2Device complexity
If conventional scheduling methods are used, then simplicity is maintained, but resource utilization becomes inefficient
Solution Approach 1:
The system performs preliminary analysis of historical execution time data before scheduling new test cases. By pre-processing execution time information and storing it in a structured format, the system prepares optimization data in advance, enabling efficient resource allocation decisions when schedules are generated without adding operational complexity
Solution Approach 2:
The scheduling system incorporates feedback mechanisms by continuously analyzing actual execution times from previous test runs and using this information to improve future scheduling decisions. The system feeds execution time data back into the scheduling algorithm, creating a closed-loop system that automatically optimizes resource utilization based on real performance data without increasing system complexity
3Measurement precision
If execution times are predicted using historical data from different IT assets, then scheduling accuracy is improved, but data reliability challenges arise
Solution Approach 1:
The system transforms execution time data from different IT assets by normalizing parameters to account for asset-specific variations. It adjusts execution time measurements based on characteristics of each IT asset, converting diverse data into a consistent format that enables accurate cross-asset predictions while maintaining reliability through parameter standardization
Data Source
AI summary
Techniques are provided for automated scheduling of software application test case execution on information technology (IT) assets. One method comprises obtaining information characterizing (i) test cases that evaluate software issues related to a software application, (ii) IT assets that execute the test cases and (iii) execution times of the test cases on the IT assets, wherein at least one execution time of a given test case on a particular IT asset comprises a predicted execution time, wherein the at least one predicted execution time is predicted using an actual execution time of the given test case on one or more different IT assets than the particular IT asset; automatically generating, using the execution times of the test cases on the IT assets, a schedule for additional executions of at least some of the test cases on the IT assets; and initiating one or more automated actions based on the schedule.