The Power of Machine Learning and Artificial Intelligence Testing

Release better quality software faster with AI and Machine Learning incorporated into your software QA testing.

Transform Your Business With AI

If you're looking to take your first step towards digital transformation, AI is your big game changer. QASource has experts who will dive into your current business model and help you implement the best AI testing solutions for your business.

What Are AI Applications?

  • AI applications are intelligent systems built on technologies like Deep Learning, Machine Learning, Computer vision, NLP, etc.
  • AI model’s performance is a combination of data collection, labeling, feature engineering, model training, and re-training.
  • AI applications are found in a wide range of industries: Finance, Healthcare, Text analytics, Robotics, Speech Recognition, Marketing, Banking, Gaming, Autonomous Vehicles, etc.

Why Is AI Testing Important?

AI is used virtually in every industry to automate monotonous business processes.

Outputs of the AI model are probabilistic and responses of the AI model for a given input can change over time.

AI works well for situations similar to that of training data but its performance is hampered in situations for which the AI model is not well-trained.

Unlike traditional QA, instead of fixed results in tests, evaluation techniques and matrices are required for AI model evaluation and testing.

AI Testing Challenges

Mobile Testing Challenge

  • Problem of BiasData used for training could be skewed or may suffer from class imbalance, which introduces bias in the model.
  • Extracting Root CauseExtracting the root cause of a prediction error in AI is generally not possible as an AI model is a combination of training data, labels, algorithms, and training parameters. Therefore, narrowing down the root cause of an error is not easy.
  • Iterative Learning of ModelsUnlike traditional software systems, where once development and thorough QA covering all verification and validation of a feature is done, it does not require retesting until there are some new code changes, AI models need constant re-training on new data. Therefore, AI models need constant evaluation and testing.
  • Non-deterministicAI/ML systems are non-deterministic, they can generate different outputs or responses for the same input on different runs. Therefore, the traditional QA approach of verifying fixed expected results does not work here.
  • Test Scenarios
    Generating test scenarios or edge scenarios for AI systems is difficult and needs solutions like metamorphic testing.

We Are the Best at Providing Outsourced QA Services To Help Clients Release Better Quality Software Products

QASource’s Artificial Intelligence Testing Services

Data Validation

  • Evaluation of quality of training datasets, including aspects such as bias and variety
  • Validation of data labels
  • Exaction and curation of validation and test datasets

AI Model Evaluation and Testing

  • Model’s prediction result analysis and evaluation
  • AI model evaluation is based on metrics like confusion matrix, AUC ROC, F1 Score, etc.
  • Sharing insights and feedback on model behavior to AI model developers

Computer Vision Application Testing

  • Thorough QA and QC for images or video data ingestion
  • Testing of data annotations, data labeling, and data ingestion format
  • Computer vision-based test automation for visual testing

NLP Applications Testing

  • Testing of NLP models’ recognition and predictions
  • Speech and NLP models evaluation based on metrics like word error rate (WER), text similarity measures such as cosine similarity, Levenshtein distance, etc

Metamorphic Testing

  • Test case generation and test results verification based on metamorphic relations, to validate the algorithm’s response to multiple inputs and their expected outputs
  • Testing metamorphic relations

Non-functional Testing

  • Data scalability and performance testing for the AI systems
  • System integration and API Testing
  • Security Testing

Chatbot Testing

Domain Testing: Chatbots are domain-specific and need to have specificities associated with their domain identified and tested upfront.

Limit Testing: Verification of how a chatbot responds to an irrelevant question and identifying the outcome when a chatbot fails.

Robotics Testing

  • It is a simulation-based behavior testing that includes debugging an algorithm, testing object detection and response, and testing defined goals.
  • Testing of hardware availability and unavailability scenarios.

Why Partner With QASource AI Testing Team?

  • Expertise in AI QA services for machine/deep learning applications, NLP, computer vision, speech recognition, and robotics
  • Computer vision and NLP-powered test automation for AI applications
  • A dedicated team of QA experts with knowledge of machine/deep learning applications, NLP, and computer vision
  • A team well-versed in AI workflows, model evaluation, and testing
  • Nearshore, offshore, and hybrid outsourcing options
  • Access to state-of-the-art testing facilities, test labs, and tools
  • Non-billable engineering leadership and US customer support
  • Access to an advanced technology group constantly improving our automation, database, DevOps, Dev, and IT capabilities

AI Testing Resources: Here Are Some More Blog Posts

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Frequently Asked Questions 

What is AI testing?

AI testing involves the verification of artificial intelligence and machine learning-based products to improve the quality, efficiency, and effectiveness of such software products. It includes data validation, model evaluation, and testing of AI applications across various industries.

Why is testing AI applications different from conventional software testing?

Testing AI applications is fundamentally different due to their probabilistic nature, reliance on vast amounts of data, and the continuous learning aspect. These factors require unique approaches, such as metamorphic testing and continuous evaluation metrics.

How does QASource approach AI testing?

QASource uses advanced techniques and methodologies, such as metamorphic testing, confusion matrix, AUC ROC, and F1 Score metrics for model evaluation, to ensure comprehensive testing of AI applications. Our approach is tailored to meet the specific needs of each AI application, whether it involves NLP, computer vision, robotics, or any other AI technology.

How can I get started with AI Testing services from QASource?

You can begin by scheduling a 30-minute free consultation with one of our AI specialists. We'll discuss your current business model, understand your testing needs, and recommend the best AI testing solutions for your business.

What are the key challenges in AI testing, and how does QASource tackle them?

Key challenges include dealing with non-deterministic outcomes, identifying bias, ensuring data quality, and creating relevant test scenarios. QASource tackles these with advanced testing methodologies, continuous learning approaches, and AI-driven testing tools.