Introducing Best Guide On Testing AI Models Beyond 2020

Introduction to Testing AI Models

Self-learning and self-thinking systems play an integral role in society. With the advent of artificial intelligence, the current trends indicate that machine learning is the fastest-growing field in computer science.

Sadly, it is not clear to some consumers who are neither ML developers nor data scientists. Yet, they listen about AI from the market and know that they need to incorporate this technology into their products.

Below are generally asked questions that we get based on the customer research about quality assurance within ML:

  • I want to execute UAT; could you please suggest full regression test cases for AI?
  • Okay, I got a model running in production; how do we ensure it doesn’t break while updating?
  • How do I assure the values that I need in the product are the right ones?

Machine learning (ML) and Artificial Intelligence (AI) create so much buzz in multiple sectors and encourage people to include such technologies in their daily lives.

Artificial intelligence advertises as a solution to all test-related problems, mainly for those who have never tested yet – those who believe that it could make software testing simple and those who feel that what we’re doing as testers is nothing more than tapping screens to make comparisons.

It is the answer for those who assume that the testing could perform without all discussions, defect reports, and metrics. After all, the name ‘Artificial Intelligence’ seems like a robotic.

Workplaces like finance, retail, technology, healthcare, and education start leveraging AI to simplify routine tasks, reduce costs, and make data-driven decisions.

In homes, AI starts controlling televisions, personal digital assistants, security cameras, home automation, and offers movie recommendations.

A Detailed Introduction to AI (Artificial Intelligence)

In simple words, AI is the potential of machines to perform activities and tasks that we consider as ‘intelligent.’ An intelligent device can do particular tasks and analyze its environment to accomplish the objective as best as possible.

In testing, AI aims to gather information about the quality and risks of an information system. AI can offer support to testing after the observation of the information system and collected data.

Further, this collected data used in reports. It even helps AI learn about it and perform well in tasks to produce better outcomes.

Usage of AI in Software Testing

The goal of AI in software testing is to make testing more efficient and smarter. Both machine learning and AI use reasoning to solve problems with automation and improve testing practices.

With the use of AI in software testing, one can reduce time-consuming manual testing. It helps the team to focus on complex structures such as the creation of innovative new features.

See also  9 Ways How AI Is Transforming Automation Testing In Great Ways

When talking about AI and testing together, most people are stuck in confusion about whether AI improves testing or testing helps AI to be better.

But in reality, AI in software testing is the process of testing of AI systems where the quality is carefully examined. However, ‘testing with AI’ is the process where artificial intelligence is used to support the testing procedures.

If we talk about ‘testing with AI,’ then it is the AI technique that can make testing tools more effective and efficient.

Hence, it looks like AI can’t survive without testing, and testing also needs AI.

Testing AI Models
Testing AI Models

AI Systems Differ from Traditional Software Systems:

Features: Software is deterministic, meaning it is pre-programmed to offer a particular output as per the given set of inputs. On the flip side, AI and ML are non-deterministic in which algorithms can behave differently while running in different ways.

Accuracy: The accuracy of software depends on the programmers’ skill set. The software can become successful if and only if developers produce output based on the required design. However, the accuracy of AI algorithms is entirely dependent on data inputs and the training set.

Programming: All software functions as per if-else conditions, looping to convert input data to output data. On the other side, the combination of different inputs and outputs is fed to the machine to help the AI learn and describe the function accordingly.

Defects: The defects credit of software goes to human intelligence or poorly-used coded functions. Inversely, AI-systems have self-healing abilities to resume operation after managing errors or exceptions.

Sequential Stages of AI Algorithms, their Failure Points & Ways to Discover with Testing

  1. Data Sources:

    It can be static or dynamic sources in AI. For example, sensors, images, texts, speech, videos,

Failure Points:

Issues of completeness, formatting, correctness, and appropriateness of source data quality can occur.

Solutions with Testing:
  • Automated checks on data quality.
  • Data transformation testing.
  • Use of aggregate and sampling strategies.
  • Capable of handling heterogeneous data while testing.
  1. Input Data Conditioning:

    It includes data lakes and big data stores.

Failure Points:

Data duplication and incorrect data load rules occur. Errors happen in data nodes partition. It may cause data drops and data truncation issues.

Solutions with Testing:
  • Performing data ingestion testing.
  • Ability to create test data sets and subset.
  • Knowledge of codes and development models needed.
  • Understanding of data required for testing.
  1. Machine Learning and Analytics:

    It consists of algorithms or cognitive learning.

Failure Points:

Understanding how data splits for testing and training. Failure can occur while understanding the data relationships between tables and entities.

Sometimes out-of-sample errors happen like experiencing a new behavior in previously unseen data sets.

Solutions with Testing:
  • Implementing regression testing, algorithm, and system testing.
  1. Visualization:

    It includes custom apps, web, bots, and connected devices.

Failure Points:

Incorrect coding rules in customized apps lead to data issues. Data reconciliation and data issues can happen between the back-end and reports.

Communication failure in APIs/middleware systems resulted in disconnected data collaboration and visualization.

Solutions with Testing:
  • Executing API testing, end-to-end functional testing with automation.
  • Testing of analytic models.
  • Testing of development models and reconciliation.
  1. Feedback:

    The feedback comes from sensors, devices, apps, and systems.

Failure Points: Usage of inaccurate coding rules in custom apps leads to data failure. False positives propagation at the feedback stage also results in incorrect predictions.

Solutions with Testing:
  • RPA testing execution.
  • OCR (optical character recognition) testing implementation.
  • Chatbot testing frameworks.
  • Performing image, speech, and NLP (natural language processing) testing.

Using the Right Testing Strategy is Good Ethics for AI Systems

As discussed above, several failure points occur in AI systems, so there should be the right test strategy in hand to mitigate the risk of failure.

Companies should understand the different stages of the AI framework. A better understanding can help them make a comprehensive test strategy with particular testing techniques throughout the whole framework.

Below are four AI use cases that need to follow for testing the functions of AI systems accurately.

1. Use Case: Testing Standalone Cognitive Features

Cognitive features are the individual technologies used to perform specific tasks to make it easier for humans. For instance: NLP is a branch of AI that aids computers to understand, manipulate, and interpret human language.

Similarly, OCR is an AI-based software used for taking images of handwritten characters as inputs and further interpret them into machine-readable texts.

Image recognition and speech recognition inputs are based on AI models. It helps to perform accurate and easier tasks. Yet, it needs testing to ensure the quality of systems.

Test scenarios for standalone cognitive features are:

Optical Character Recognition (OCR):
  • Check optical word recognition (OWR) and OCR using word inputs or character for the system to recognize.
  • Testing deep learning ensures that the system can recognize words or characters from speckled, skewed, or when color converts to grayscale documents.
  • Test supervised learning to analyze whether the system can remember words from written, printed, or cursive scripts.
Image Recognition:
  • Check the image recognition algorithm via features and basic forms.
  • Test supervised learning by blurring or distorting the image to understand the extent of recognition through algorithms.
  • Perform in-depth learning testing to observe whether the system can detect an object’s portion in a smaller or larger image canvas.
  • Pattern recognition testing by replacing cartoons with real images.
Speech Recognition Inputs:
  • Perform necessary testing on the software to ensure that speech recognition can recognize speech inputs.
  • Run the deep learning testing to clear the difference between ‘New York’ and ‘Newark.’
  • Pattern recognition testing identifies whether the system can analyze unique phrases if repeated in the same or different accents.
  • Test how speech translates and gives a response.
Natural Language Processing:
  • Conduct testing for true positives, true negatives, false negatives, and false positives.
  • Perform testing for ‘recall’ and ‘precision’ return of the keyword.

2. Use Case: Testing Platforms of AI

Performing testing on platforms, specifically that hosts AI frameworks are very challenging. It applies and follows various steps during functional testing.

Test Scenarios for AI Platforms:

Algorithm Testing:
  • Split input data for algorithm and learning.
  • If the algorithms use unclear datasets in which individual input-output is unknown, testing is a must for that software.
  • Test the cumulative accuracy of test positives, test negatives, false positives, and false negatives.
Data Source and Conditioning Testing:
  • Validate the data quality from various systems like completeness, correctness, and appropriateness. You can also verify the format checks and analyze different patterns.
  • Implement tests on both positive and negative scenarios.
  • Verify transformational logics and rules used on raw data to obtain the desired output format.
  • Verification of output programs or queries to make sure it provides the intended data output.
API Integration:
  • Validate request-response pairs.
  • Check input requests and responses from each API (application programming interface).
  • Conduct integration testing of algorithms and APIs and verify the visualization of outputs.
Regression/System Testing:
  • Verify system security with both static and dynamic scenarios.
  • Perform user-interface and regression testing on systems.
  • End-to-end implementation testing for preparing use cases.

3. Use Case: ML-based analytical Models Testing

Companies develop analytical models for predicting the future based on past data, historical data analysis, and visualization.

The other reason to build an ML-based analytical model is prescribing a course of action from past data. One must have a validation strategy for testing the analytical models.

For this, you can split the historical data into ‘train’ and ‘test’ datasets. The next step is to test and train the model as per the generated datasets.

In the final step, you can prepare the report to examine the model’s accuracy by using several generated scenarios.

For testing a model, you need to devise the right strategy to subset and split historical data using in-depth knowledge of the development model.

You need to understand the code and realize how it works on data. Get end-to-end evaluation strategy for training and recreating models in test environments.

You can customize the test automation process to optimize the throughput of testing, evaluate the model, and generate reports.

Further visit: 5 Incredible Ways -Artificial Intelligence Spikes in Demand by Digital Space

4. Use Case: AI-Powered Solutions Testing

Testing on Chatbot Framework:
  • Test the chatbot framework with semantically equivalent sentences. Create a library with automation to serve this purpose.
  • Manage configurations of basic and advanced semantic semantically parallel sentences with complex words, formal and informal tones.
  • Automate end-to-end processes.
  • Prepare automated scripts in Python language for execution.
Testing on RPA Framework:
  • Use functional testing tools (Robot Class, Selenium, Sikuli) or open-source automation tools for multiple applications.
  • Use flexible test scripts to convert machine language programming to high-level language for functional automation.
  • Use a mix of text, voice, pattern, optical character recognition, and image testing techniques with functional automation for reliable end-to-end testing applications.

In a Nutshell

Artificial intelligence frameworks rely on five learning phases from several data sources: input data conditioning, machine learning and analytics, visualization, and feedback. Each stage poses a risk of failure that needs to be identified using different techniques.

Thus, QA departments must define clear test strategies to get the testing done of AI systems. Many hurdles can occur during testing.

Still, there is a need to understand the essential use cases mentioned above for AI-powered solutions, standalone cognitive features, AI platforms, and ML-based analytical models.

Getting a comprehensive test strategy is crucial for software testing companies to help businesses leverage their AI frameworks, reduced cost, and minimize failures. With an effective test strategy in AI, one can improve the quality and accuracy of products and services.

Author Bio:

Kanika Vatsyayan is Vice-President Strategies at BugRaptors, a certified QA testing services company. She loves to share her knowledge with others through blogging.

Being a voracious blogger, she published countless informative blogs to educate the audience about automation and manual testing.

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