Testing Analytics Flashcards
Why is testing needed?
Goal of testing:
- Reduce risk - by proactively finding and eliminating problems which would most greatly impact customers using the software.
- Prioritize testing - of areas which may have the largest impact on the customer. Develop tests to find potential errors.
Potential User Impacts
- Impact by the frequency of an error
- Impact by the severity of the problem
Things that annoy the end-user every time they use your software, or things that rarely happen, but have great negative consequences.
Types of Testing
Types of Software Testing:
- Black box
- White box
- Acceptance
- Automated
- Regression
- Functional
- Exploratory
Black box testing
Testing an output of a given input
White box testing
Testing the source code - use knowledge of the source code to develop your tests.
Advantages - discovering hidden bugs more efficiently, code optimization, and fast problem spotting.
Disadvantages - coding knowledge required, code access req., and focus on existing software not missing functionalities.
Acceptance testing
Testing what is expected vs. what actually happens.
Test actual req. or expectations of the customer
Advantages - easy to spot usability issues early on
Disadvantages - needs a well defined test audience, and it’s time consuming to set-up.
Automated Testing
Recurring standardized tests with scripts
More and more testing is moving to automated testing because manually running tests is tedious and more prone to error.
Regression Testing
Test to verify the system still works as it did before the change.
Purpose - testing is to make sure the software doesn’t regress in functionality. (regress-ion)
Functional Testing
Test all things related to functionality
Does the system do what it’s supposed to do from a functional perspective.
Exploratory Testing
Tests within certain areas, no specified test cases.
Exploring the application for applications or behaviors not acting as expected.
Sometimes recorded since there is no script and errors can be hard to recreate
What are the 4 stages of the testing process?
- Development of a test plan
- Design tests
- Test creation and execution
- Log results
Bugs are then prioritized and fixed
Testing to “break the software”
A way of performance testing or functional testing
Pushing the system to extremes - adopt the following attitude: you’re looking for any reason not to buy the software.
Predictive Analytics vs.
Descriptive and Prescriptive Analytics
The 7 Steps of Predictive Analytics
- Define the project
- Data collection
- Data analysis
- Statistics
- Data modeling
- Model Deployment
- Monitoring
Defining the project
This is the first step of the Predictive Analytics model. Here you will have a clear-cut definition of the outcome of the project, the business objectives, the scope of the effort, identifying data sets and more.
Collecting the data
This is the second step of the process wherein you will be mining for the data from multiple sources and prepare the Predictive Analytics mode andprovide a complete overview of the entire process.
Analyzing the data
This is the process that includes the various steps of inspection, cleaning, modeling of the data for discovering the objective and help to reach at a conclusion.
Deploying statistics
Here you will be working on validating the assumptions and hypothesis and testing it using the standard statistical models.
Data modeling
This is the process that provides the ability to create automatic predictive models of the future. You can also create a set of models and choose the most optimal one.
Model deployment
This is the step in which you will be deploying the analytical results into your everyday business operations helping to get results, reports and the output of the automated decisions.
Monitoring the model
The models are reviewed in order to ensure the performance of it is going in the right direction.
