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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.

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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 Bias

Data used for training could be skewed or may suffer from class imbalance, which introduces bias in the model.

Extracting Root Cause

Extracting 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 Models

Unlike 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-deterministic

AI/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.

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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
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AI Testing Resources:
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