Robotic Regression Testing for Smart Meters
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Solution Overview
Problem
Manual testing of smart devices, such as electric and gas meters, is inefficient and prone to human errors, leading to incomplete firmware testing and increased risk due to the inability to perform comprehensive assessments across multiple devices and firmware versions.
Innovation Solution
Implementing a robotics system with a wall-mounted gantry robot to automate end-to-end device testing, including physical perturbations, and a computing system for task scheduling and optimization, which merges and adjusts test plans based on performance data and learned inferences to simulate human inconsistencies and improve testing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing is used by workers to push buttons on devices, then human operators can perform testing tasks, but testing efficiency is low and human errors occur leading to incomplete firmware testing
Solution Approach 1:
The patent replaces manual mechanical button pressing with an automated robotic system that uses vision guidance and automated actuators to interact with device interfaces. This substitution eliminates human error while maintaining testing capability, directly resolving the contradiction between reliability and productivity in the testing process
Solution Approach 2:
The system incorporates self-learning capabilities where the robot observes and learns from demonstrated human operations, then autonomously performs and optimizes testing tasks without continuous human intervention. This self-service approach enables the system to maintain high reliability while achieving scalable productivity
2Loss of time
If sample testing is used to test firmware functions, then testing time is reduced, but substantial risk accumulates due to inability to perform comprehensive assessment
Solution Approach 1:
The testing system dynamically adjusts the scope and depth of testing based on risk assessment algorithms and historical data. High-risk firmware changes trigger comprehensive automated testing sequences, while low-risk changes receive streamlined testing, enabling the system to optimize between testing time and completeness based on real-time conditions
Solution Approach 2:
The system implements continuous feedback loops where test results, performance data, and anomaly detection inform subsequent testing decisions. This feedback mechanism enables the system to automatically expand testing scope when risks are detected and reduce scope when confidence is high, dynamically balancing testing time against completeness
3Reliability
If multiple distinct test plans are executed separately, then comprehensive device coverage is achieved, but task scheduling complexity increases and testing efficiency decreases
Solution Approach 1:
The system merges multiple distinct test plans into a unified automated execution framework that intelligently sequences and coordinates testing tasks. By consolidating test plans and eliminating redundant operations through automated analysis, the system maintains comprehensive device coverage while dramatically reducing scheduling complexity
Solution Approach 2:
The unified test framework segments testing tasks into modular, independently executable units that can be dynamically assembled based on device characteristics and firmware versions. This segmentation enables flexible reconfiguration of test sequences without increasing overall scheduling complexity, as each module is self-contained and clearly defined
4Ease of operation
If human testers manually perform push buttons during testing, then testing can be performed, but human inconsistencies occur such as pressing buttons more than needed or imprecise button press locations
Solution Approach 1:
The system replaces imprecise human mechanical interactions with automated robotic actuators controlled by vision systems. These actuators can precisely locate and press buttons with consistent force and positioning, eliminating the precision errors and operational inconsistencies inherent in manual testing while maintaining ease of operation through automated control
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating multiple distinct test plans using a computing system. The system merges the each of the test plans to produce a first test plan and obtains performance data about automated execution of the first test plan by a robotics system. The system generates learned inferences for determining predictions about execution of the first test plan based on analysis of the performance data and adjusts the first test plan based on the learned inferences. The system generates a second test plan for execution by the computing system based on adjustments to the first test plan and performs the second test plan to test multiple devices using the robotics system.