17551345. REGRESSION TESTING FOR WEB APPLICATIONS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
REGRESSION TESTING FOR WEB APPLICATIONS
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
REGRESSION TESTING FOR WEB APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17551345 titled 'REGRESSION TESTING FOR WEB APPLICATIONS
Simplified Explanation
The patent application describes a method for training a predictive model using network traffic and data change messages from a web application. The model is trained to predict data changes resulting from API calls in the network traffic.
- The existing web application's network traffic and data change messages are used to train a predictive model.
- The upgraded version of the web application is used to generate real data changes by replaying the network traffic.
- The network traffic is then applied to the predictive model to generate predicted data change messages.
- The predicted data change messages are compared with the real data change messages to identify any inconsistencies.
- Inconsistencies in the predicted data change messages can indicate possible functional degradation in one or more existing APIs.
Potential applications of this technology:
- Monitoring and troubleshooting web applications in a production environment.
- Identifying and addressing functional degradation in existing APIs.
- Improving the accuracy of predictive models for data changes resulting from API calls.
Problems solved by this technology:
- Detecting and addressing functional degradation in existing APIs.
- Improving the efficiency of monitoring and troubleshooting web applications.
- Enhancing the accuracy of predictive models for data changes in a production environment.
Benefits of this technology:
- Early detection of functional degradation in APIs, allowing for timely resolution.
- Improved efficiency in monitoring and troubleshooting web applications.
- More accurate prediction of data changes resulting from API calls.
Original Abstract Submitted
Training a predict model with network traffic and data change messages generated by an existing web application running in a production environment. The predict model being is trained to predict data changes resulted from API calls embodied in network traffic. A stream of network traffic of the existing web application is replayed with an upgraded version of the existing web application to generate real data changes. The stream of network traffic is applied to the predict model to generate predicted data change messages. The predicted data change messages are comparing with real data change messages representing the real data changes. One or more existing APIs is identified as being possibly functionally degraded based on any inconsistency of the predicted data change messages with the real data change messages.
