Thus justifying its inclusion into projects to minimize bugs and mitigate issues. It is recommended to add automated tests into ML projects to alleviate such difficulties and issues more rapidly.
What Is Automated Testing?
An automated tester uses specific software to conduct testing of programs and identify any bugs, which has been around since early 1990 but has recently seen increased popularity with Agile Development process and Continuous Integration becoming mainstream practices.
Testing is an integral component of any development project, helping identify bugs and defects earlier in the life cycle and saving both time and money down the line.
Automation tests have also proven more precise due to reduced human error risks.
Automated testing offers several benefits over manual testing, including:
- Reduced developer effort and overall costs
- Improved quality and consistency
- Faster release cycles
- Easy distribution of tests across multiple devices or locations
- Better reporting capabilities and others
Automated Testing team Examples: Examples to help test Automated
Automating testing typically brings to mind software testing as the premier means for assuring quality software applications, though automated testing covers not only this domain; other testing types like security, performance, and hardware testing could be included too.
Hardware testing ensures a product leaves its factory with quality assurance through automated hardware and software tests that validate quality before leaving factory floors, known collectively as UUT testing.
Automated hardware tests may include mechanical stimulation such as vibration, actuation pressure changes in temperature fluctuations, or electrical stimulation such as power supplies or triggers; then, analysis, this data is collected, and reports are created as necessary.
Cyber testing (also referred to as security testing) entails performing automated tests against software programs, network real devices, and entire IT systems in order to identify vulnerabilities and test for weaknesses.
Testing methods might include looking out for vulnerable systems (for instance, old versions of web servers), brute forcing password forms using brute force attacks or dictionary attacks, or overloading a system using DDoS/ Brute Force attacks in order to discover whether information has leaked (DDoS or Brute Force attacks).
Software testing platform involves performing tests to test an application under load, its primary goal being the discovery and removal of performance bottlenecks within software applications.
Performance testing may take on various forms:
Stress testing your app with expected users and workload is also recommended, while software testing remains one of the most useful forms of automated testing; commonly seen examples of software testing include:
Unit tests are performed to examine components or functions within software applications. By isolating one function at a time for testing purposes, unit tests allow you to see how its operations impact an application as a whole.
Integrity tests: Integration tests follow unit tests in order to detect any inconsistencies between software components.
Acceptance Tests: Acceptance tests serve the primary goal of verifying whether an application performs as intended for end-user consumption in similar environments to production.Test conventional software or machine learning projects.
There is no universally applicable strategy for testing machine learning projects; testing them requires more planning due to ML projects requiring models and data not easily predetermined in advance, unlike most software testing processes that include production tests only.
For optimal testing in machine learning projects, it may also include model/data tests along with production checks as part of its portfolio of tests.
Modern machine learning projects present more uncertainty than software of the past, we dont always know whether an idea can technically work and need to conduct extensive research before making our decision.
This uncertainty can hinder good software development practices such as testing; we do not enjoy spending our time testing early ML projects that may or may not receive approval to move forward with development.
More time spent without testing creates more technical debt; we must place increasing emphasis on improving testing practices and repaying technical debt as our ML projects mature.Data testing is an integral component of Machine Learning testing since its behavior differs significantly from traditional software systems in that its functionality is learned rather than specified in code.
We cannot assume that an optimal model will result from flawed data.
As part of an effective Machine Learning (ML) solution, its imperative that we ensure the systems continue functioning effectively after development or launch and into production environments.
Software often only gets tested during its development phase before moving it onto production - however, in ML projects, external factors may alter data sources or cause data changes over time, necessitating continuous testing using dashboards and output logs for system monitoring purposes.
Machine Learning Challenges
Testing machine learning (ML) is more challenging than conventional software testing; we have previously noted this.
There are various components within an ML pipeline that must also be taken into consideration; anomalous predictions might not always result from model error but can instead come from data input that has an unexpected pattern or shape.
One of the major concerns surrounding an ML project includes:
- Data issues: Missing values, data distribution shift, setup problems, the impossibility of reproducing input data, inefficient data architecture, etc.
- Model issues: Low model quality, large model, different package versions, etc.
- Deployment issues: unstable environment, broken code, training-serving skew, etc.
Because of that, in this article, we'll propose some tests that can mitigate the effect of such problems.
Issues associated with deployment include an unreliable environment, broken codes, and an imbalance in the training/serving ratio.
We will explore some ways of mitigating such challenges here.
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Types Of Automated Tests In Machine Learning
Automated Testing in Machine Learning does not offer one universal rule to classify automated tests; thus, we have broken this articles automated tests into different categories for easier reference.
Smoke Testing
Smoke Testing One of the simplest types of testing, smoke testing, should be implemented as soon as the project starts to ensure the code works as it should and remains bug-free.
While this test might seem inconsequential, its significance cannot be overestimated when dealing with multi-language projects such as Minecraft or even an Open ML project.
ML projects typically employ numerous libraries and packages from third parties; these updates often alter functionalities within these packages even though their code remains the same.
We may wish to opt for older versions that have proven more robust. Therefore, using older packages might also prove advantageous.
One effective method of testing this is by creating a dependency.txt with all dependencies and then performing smoke testing in multiple test environments - this ensures code can run in more than just our current environment and install any needed dependencies, installing older dependencies from previous projects is often problematic as well.
Implementation
Many teams rely on smoke tests to make sure code runs as intended after someone commits a change, with these smoke tests often set up using Jenkins, GitHub actions, or another Continuous Integration platform.
Test Your Unit
Unit testing is the next testing step after smoke testing. Unit tests involve isolating components that can be individually evaluated; their purpose is to break code down into blocks that can then be individually examined before being run through their set of unit tests.
Unit tests can help early development teams detect bugs more efficiently by isolating isolated code pieces instead of trying to analyze them all at once.
Furthermore, this helps design better code since performing unit tests could reveal that certain areas were poorly constructed or even missing altogether.
Under normal conditions, unit testing should start immediately upon breaking code up into functions and classes.
While writing unit tests early would save time in terms of development costs, writing them later might mean it becomes necessary or too late for the deployment of an already-developed system.
Implementation
A typical pattern for writing unit test functions includes several steps. These are:
Identification and Verification of Data Inputs The first step to testing any logic should be identifying input data to perform it and obtain results and, finally, defining desired output results.
Unit tests can be utilized in numerous ways. They are applicable for any part of code which can be logically separable, as well as being used to check input data, models, or features.
Unittest is the standard unit testing framework in Python and offers automated tests as well as sharing of setup/shutdown code between setup/shut down processes, the collection/aggregation/aggregation of tests, and independent reporting capabilities.
Unittest also features the unittest. The mock module enables mock objects to serve as replacements for parts of an application under test, making assertions on how they have been utilized and tracking how often they were employed; Mock classes designed specifically to replace test duplicates in your entire project can also track usage so we can claim what code has been applied against each mock object.
Pytest is another library designed to make unit testing simpler. Based on three main concepts - test functions, assertions, and set up - this library facilitates test automation tools via its automatic running functionality.
Integrity Testing
Integrity testing should follow unit tests in order to test how components interact, so integration testing may also be employed as part of this step.
Integrity testing does not refer to testing an entire ML project but instead involves testing only specific elements within it.
Integration testing may incorporate numerous unit tests that all come together into one test. Integration tests primary objective is to validate that modules work as designed together while meeting system and model requirements, unlike unit tests which can be run separately; integration tests only run when our pipeline executes; they may fail even though all unit tests passed successfully.
Software testing traditionally occurs only during development; once in production, it should have already been scrutinized.
Integration tests form part of ML production pipelines, while monitoring logic must also be in place if these pipelines rarely get executed.
Read More: Machine Learning, AI, And Deep Learning
Implementation
Integrity tests can be written easily as "try-except" conditions within code, making integration tests accessible even to novice developers and early-stage ML project development teams.
Integration tests can also be utilized for other uses. We can, for instance, utilize integration tests to analyze many other examples, such as target variable distribution or NULL values, as well as test model performance.
There are packages designed to assist with writing integration tests. Pytest provides end-to-end and integration testing capabilities of feature pipelines.
Regression Testing
Our aim with regression testing should be to prevent it from exposing us to bugs we have already addressed and fixed or any new code changes from reintroducing old bugs that we know exist.
Therefore its advisable that whenever submitting bug fixes for submission that a test be run against it to ensure effectiveness and prevent future regressions.
As data sets become more complex and models require regular retraining, regression testing in machine learning projects becomes an invaluable asset to ensure performance remains optimal.
When our models fail to make decisions when presented with challenging input samples, integration of such tests could ensure our pipeline stays intact - it might include one every time our model fails.
Implementation
When working on a computer vision model with subsample images that contain banding noise, our model could produce much poorer results than expected.
Since fixing our camera may have temporarily malfunctioned and caused these types of images, fixing our model might prove challenging; thus, it would be wiser to create a regression test just in case something similar were to happen again in future projects.
Regression testing can detect bugs that havent yet appeared but might in the future. When building machine learning systems for self-driving cars, for instance, we must consider all scenarios that might come up in real life, even though no specific data exist to test our model with.
One such scenario might involve receiving subsample images with Gaussian noise present - just one way of testing what could come about later!
Given their differences and dependence, there is no standard library to write regression tests.
Data Testing
Data testing refers to any test in an ML Project which involves data. Data could potentially feature in all previous tests except smoke testing; thus, this section intends to offer examples and ideas about what we could test when working with data.
This section is particularly crucial, given that data used in most ML projects greatly impacts their behavior. Below well discuss several data validation tests.
- Verifying some data attributes would be useful. It should be expected, for instance, that an individual will stand higher than 3 meters; images usually reveal which attributes should be expected when dealing with images; otherwise, statistical significance can help draw inferences regarding future outcomes from data analyzed by using statistics alone.
- Knowledge is power when it comes to engineering costs and time commitments; knowing their significance helps guide future decisions about new features that incur engineering expenses and take up valuable engineering resources. Testing methods provide another useful means of measuring feature importance each time one is introduced into production.
- Test new data and features against their costs to establish whether their implementation would justify them. Our aim should be to evaluate whether adding another feature increases inference latency or RAM usage - this way, we can decide whether or not to include it in our Machine Learning project.
- Data that is prohibited or inaccurate: We strive to make sure the data will not cause legal complications in the future while also verifying its source as being sustainable and verifying vendor(s)/sources of origination of said data (sustainable vendor sourcing or sources). Whenever pipeline data access is tested, we ensure its security is checked.
Implementation
Most data tests involve integration and unit tests. To successfully implement them, it is imperative that best practices for unit and integration testing be utilized.
When data testing, expectations need to be clearly laid out before beginning your journey with it.
Great Expectations is an invaluable way to manage data testing in any project, helping teams eliminate pipeline debt through data testing and other means.
Model testing also plays a part in its operation.
Model Testing
Model testing, similar to data testing, can be utilized during integration, regression, unit, and other types of software testing.
Since models rarely appear as part of software applications for testing purposes, model testing stands out among its counterparts as something special and individualistic. Below we list several useful tests for model testing:
Maintaining a version control system for specifications is of great importance, both to track their changes as they evolve as well as being able to identify which code was utilized to achieve specific results and check those before pushing new versions onto main branches.
Overfitting of Model: Make sure there is no overfitting in your model using appropriate validation techniques and by performing an out-of-sample test on it to check its fit with reality.
Utilize hyperparameter tuning techniques such as grid searching or more sophisticated metaheuristics when setting hyperparameter values.
Automated tests should use grid searching or random searching and be initiated when an updated feature becomes available.
Model Staleness can have a severely detrimental impact on applications of financial machine learning and content recommendation, among others.
Machine Learning models become obsolete if they do not remain up to date; to assess this impact on predictions accurately. Therefore, testing old models against current ones to understand the aging processes of models is vitally important to update them regularly and consistently successfully.
Implementation
There are various tools designed specifically to aid model testing.
Deep Checks is an intuitive Python package for validating machine learning (ML) data and models with minimum effort, including performance checks, integrity analysis of data integrity checks, and mismatched distribution checks.
ChecklistThe checkList testing method does not focus on either model type or task but instead tests each model individually via three types of tests:
- Minimum Functionality test (MFT): intended to create small and focused testing datasets, and are particularly useful for detecting when ML models use shortcuts to handle complex inputs without actually mastering the learning capability.
- Invariance test (INV): it's about applying label-preserving perturbations to inputs and expecting that the model prediction will remain the same. For instance, changing location names with NER capability for Sentiment analysis tasks.
- Directional Expectation test (DIR): similar to INV except that the label is expected to change in a certain way. For example, adding the negative word in the sentence and observing sentiment changes.
Monitoring Machine Learning Tests
Proper Machine Learning testing can go a long way toward guaranteeing its ongoing functionality and success. Keep tabs on it with dashboards displaying relevant statistics and charts, alerting you if anything unusual arises in its operation.
Monitoring serving systems, pipelines for training, and input data when working on machine learning (ML) projects is of vital importance.
Automated tests could prove especially valuable. Below are several such examples:
- Dependency And Source Changes: typically while an ML system works in production, it consumes data from a wide variety of sources to generate useful features. Partial disruptions, version upgrades, and other changes in the source system can drastically disrupt the model training. Therefore it's useful to implement some tests which will monitor dependencies and changes in the data source.
- Monitoring Data In Production: most of the tests that we've discussed under the model tests section can be implemented as monitoring tests in production. Some of them are related to input data variance, data distribution shifts, anomalies in the outputs, etc.
- Monitoring Models In Production: similarly as for monitoring data tests, most of the model tests were covered in the section about model tests. Some of them are about monitoring the staleness of the model, changes in the training speed, serving latency, RAM usage, and similar.
Implementation
Aporia is an invaluable addition to machine-learning projects, serving to monitor models in production using its Monitor Builder feature.
Data scientists can use Aporias real-time alert system and get real-time monitoring alerts so they can investigate further to ascertain the root cause.
Arize AI provides professionals in machine learning (ML) the tools necessary for diagnosing and detecting model issues quickly and easily, helping to understand why machine learning models behave this way in real-life scenarios.
Arize AIs main aim is monitoring machine learning models closely while offering explanations, solving any related problems quickly, and optimizing them further.
WhyLabs is an innovative data science and engineering tool designed to give data scientists and engineers insight into models deployed by them, as well as gain access to insights gained from data.
Integration is easy with Python or Java, and little maintenance is needed - whyLabs even enables developers to monitor machine learning deployments while keeping logs of real-time log management with ease!
Automated Testing Tools
Automated Testing Tools - TLDR There are various automated testing tools that can aid in creating test logic and structure.
As discussed previously, this section intends to summarize these tests by listing what types of exams they include.
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Conclusion of Article
Machine Learning testing is an emerging topic and ever-evolving field due to ML systems becoming ever more sophisticated and thus needing sophisticated testing solutions.
As part of our article, we provided various approaches and tools that can assist with testing ML projects as well as tools that may assist with integrating testing logic within them.
This articles resources will assist in increasing your knowledge about available tools and automated tests.
