Machine Learning is a study of applying algorithms, behavioural data sets, and statistics to make your system learn by itself without any external help and procedure. As the Machine Learning model does not produce a concrete result, it generates approximate results or contingencies from your given dataset.
The earlier software system was human-driven, where we wrote code and logic, and the machine validated the logic and checked for the desired behaviour of the system and program. Our desired testing was based on the written logic and expected behaviour. But when it comes to testing for machine learning systems, we provide a certain set of behaviours as a training example to produce the logic of the system, and ensure that the system understands the logic and develops the model according to the desired behaviour.
How to write a model test
Model testing is a technique where any software's runtime behaviour is recorded and tested under some dataset and prediction table that the model has already predicted.
Some model-based testing scenarios are used to describe numerous aspects of the Machine Learning model.
The way to test the model
- Test the basic logic of the model.
- Manage the performance using the concept of manual testing.
- Work on the accuracy of the model.
- Check the performance on the real data, try to use unit testing.
Pre-train tests: As per the name, pre-train testing is the testing technique that allows you to catch the bugs before even running the model. It checks whether there is any label missing in your training and validation dataset; and it does not require any running parameter.
The pre-train testing goal is to avoid wastage during training jobs.
Problem statement of pre-train testing:
- Check leakage label in your training dataset and validation dataset.
- Check the single gradient to find the loss of data.
- Check the shape of the dataset to ensure the alignment of data.
Post Train Testing is used to check whether it performs all the validations correctly or not. The main purpose of post-train testing is to validate the logic behind the algorithm and find out the bugs, if any.
The post-train testing deals with the job behaviour.
They are basically of three types.
- Invariant tests
- Directional tests
- Minimum functional tests
Invariant Testing is the testing technique where we check how the input data is changing without affecting the entire performance of the Machine Learning model. Here each input model is paired with the prediction and maintains consistency.
Invariant testing provides a logical guarantee about the application; this is a very low testing technique. This type of testing is mainly observed in Domain-Driven Design (DDD). Invariant testing follows three basic steps:
- Identify invariants.
- Enforce invariants.
- Refactor necessary invariants.
Directional testing is a type of hypothesis testing where a direction of testing is specified earlier to the testing. This testing technique is also known as a one-tailed test. Directional testing is way more powerful than the non-directional or invariant testing technique.
Unlike invariant testing, perturbation can change the outcome of the model in the provided input.
Minimum functional test
Functional testing is used to check whether the software or model is working according to the pre-requisite dataset or not. This uses the black box testing technique.
Types of functional testing:
- Unit testing
- Smoke testing
- Sanity testing
- Usability testing
- Regression testing
- Integration testing
The minimum functional testing model works in a similar manner to a traditional unit testing technique where the data is classified into different components, and the testing is applied over those components.
Ways to perform functional testing:
- Testing based on user requirements.
- Testing based on business requirements.
Understanding the Model Development Pipeline
The pipelining concept in machine learning is used to automate the workflows. Machine Learning pipelines are iterative processes, repeated one after the another to improve the algorithm's accuracy and model, and achieve the required successful solution.
An evaluation of the Model development pipeline includes the following steps:
- Pre-Train Test.
- Post-Train Test.
- Train model.
- Evaluation of model.
- Review and approval of dataset.
Benefits of Model Testing:
- Easy maintenance.
- Less cost.
- Early detection.
- Less time-consuming.
- More job satisfaction.
Issues while performing Model-Based Testing in Machine Learning
While working over any model, there are many shortcomings we have to deal with, which can be due to a design issue or implementation issues. Here are some drawbacks of the Model-Based Testing Technique:
- Deep understanding of problem statement is required.
- Different skill sets are required.
- More emphasis is placed on a learning curve.
- More human power is required.
Adding testing in Machine Learning
When it comes to machine learning, almost every library used in Machine Learning modeling is well tested. When you make a code call, it uses the model predict in your machine learning algorithm, and it assures you that all the layers in the method and function are calling other functions at an invariant level. This model prediction helps you to determine the function working together to deliver the required result set using the test dataset and input predictions.
There is always something to add to the Machine Learning libraries as they are not perfect. The initial test of the baseline is reasonable, and there is much more you can add to it as per the requirement. While working on the library, you can eventually find out the bug and limitation over the interface.
The complete testing procedure ends when all the functional and non-functional requirements of the product are fulfilled. The test case needs to be executed.
There are five test case parameters we have to deal with:
- The initial state of product or preconditions.
- Data management
- Input dataset.
- Predicted output.
- Expected output.
Different types of testing Techniques
The main motive to perform the testing is to find the error and secure the system from future failure. The tester follows different testing techniques to assure the complete success of the system.
The main type of testing
- Unit testing: The developer performs this to check whether the individual component of the model is working in accordance with the user requirement or not. It calls each unit and then validates each unit, returning the required value.
- Regression testing: Regression testing ensures that even after adding the component or module, the overall model is not affected, and it works fine even after several modifications.
- Alpha testing: This is the testing performed just before the deployment of the product. Alpha testing is also known as validation testing and comes under acceptance testing.
- Beta testing: Beta testing or usability testing is released to a few members only for testing purposes. This release is deployed several times to match the requirements of the user and validate them accordingly.
- Integration testing: In Integration testing, the result set is taken from the unit testing, and the combination makes the program structure of the produced output. It helps the functional module to work together efficiently to produce the required output. It makes sure that the necessary standards of the system and model are met.
Integration Testing can be classified into two main testing mechanisms
- Black Box Testing: Black Box Testing is used for validation testing techniques.
- White Box Testing: White Box Testing is used for verification testing techniques.
Stress testing: Stress testing is a thorough testing technique where we follow deliberately intense mechanisms. It checks unfavourable conditions that might occur for the system and then checks how the modules react to those conditions.
Testing is performed beyond the simple operation and integration testing capacity. It verifies the system's stability, maintains the reliability of the system, and validates the correctness of the system.
What is predictive analysis, and what are its uses
Predictive analysis is a branch of Advance analytics, where we predict the future events using past values and datasets.
Predictive analysis in a simple way is the analysis of the future, and makes different predictions over the historical data. Many organizations turn to predictive analysis to make the correct use of data to produce valuable insight in faster, cheaper, and easier ways.
How can predictive analysis be used?
Predictive analytics can be used to reduce the risk, optimize operations, increase revenue, and develop valuable insights.
Where is predictive analysis used?
- Retail sector.
- Banking and financial sector.
- Oil, gas & power utility sector.
- Health Insurance sector.
- Manufacturing sector.
- Public sector and government sector.
Difference between Machine Learning and Predictive Analysis
To understand the depth of the topic, here is the difference between Machine Learning and Predictive Analysis.
|Machine Learning||Predictive Analysis|
|Machine Learning is used to solve many complex problems using different ML models.||Predictive analysis is used to predict the future outcomes, where it utilizes the past data.|
|The Machine Learning model adapts and learns from the experience and datasets.||The predictive analysis does not adapt the dataset.|
|In Machine Learning, human intervention is not required.||In Predictive Analysis, we are required to program the system with the help of human intervention.|
|Machine Learning is said to be the data-driven approach because it depends on the dataset.||Predictive analysis is not a data-driven approach.|
What does the tester need to know?
A tester should be aware of the following considerations:
- The tester should have complete knowledge of various scenarios like the best case, average case, worst-case scenarios, how the system behaves, and how its learning graph varies.
- What is the expected output, and what is the acceptable output for each test case?
- The tester is not required to know how the model works; and just needs to validate the test cases, learning model, and required scenarios.
- The tester should be an expert in communicating test results in the form of statistical outputs.
- The tester should easily validate the algorithm and dataset and control the calculations according to the training data.
Best practices of Testing for Machine Learning in Non-Deterministic applications
Let us first understand what a Non-Deterministic Application is.
A Non-Deterministic system is a system in which the final result cannot be predicted because there are multiple possible ways and outcomes for each input. To identify the correct result, we need to perform a certain set of operations.
When dealing with the theoretical concept, the Non-Deterministic model is more useful than the deterministic one; therefore, in designing the system, sometimes we adopt a Non-deterministic approach and then move to a deterministic one.
Best Practice for Testing Non-Deterministic Applications:
- While testing, the Non-deterministic model performs continuous Integration and testing.
- Use a model-based testing approach.
- Use an augmented approach as needes by the non-deterministic model.
- Use a test asset management system, and treat them as first-class products.
- When dealing with a large set of data, perform testing on each operation at least once.
- Test all the illegal sequences of inputs with their correct response set of data.
- Always perform unit testing with extreme aberrant points.
The base goal of Machine Learning testing:
- QoS or Quality of Service, the main motive to provide the quality of the service to the user or the customer, can be said to be Quality Assurance.
- Remove all the defects and errors from the design implementation to avoid future consequences and issues.
- Find the bugs at the early stage of the project lifecycle.
What is the importance of testing in a Machine Learning project?
Small misconceptions bring a lot of issues in the development lifecycle, and defects at the initial stage of product development lifecycle can cause collateral damage to the project or complete crashing of the project. Testing helps to identify the requirements, issues, and errors at the initial stage of the product development lifecycle.
- Testing helps to discover the defects and bugs before deploying the project, software, or system.
- The system becomes more reliable and scalable.
- More thorough checking of software provides more high-performance and more chances of successful deployment.
- It makes the system easy to use and gives more customer satisfaction.
- It improves the quality of the product and its efficiency.
- There is increased success rate and an easier learning graph.
This article is an attempt to cover the basic concepts for the tester in Machine Learning. It talks about testing mechanisms, and indicates how to determine the best fit for your requirement. You will learn about different types of model tests, model test deployment pipeline, and different testing techniques. You will get insights about the Machine learning test automation tools and requirements; and understand the most important aspect of machine Learning testing— data, dataset, and learning graphs.
The tester is made aware of the Machine Learning project's basic requirement, deep understanding of the datasets, and how to organize the data so that it acts according to the user demand. If you work according to the procedure, the result will be accurate to some point.
The model should be more responsive and informative to develop business insights. As part of the last phase of the project development lifecycle, testing is a very important and critical step to be followed.