In a QA cycle, we capture various metrics to gauge the testing we have done against the baseline values according to industry standards. In this article, we are using an AI model to make the release sign-off decision, calculated with automated metrics.

AI-Enabled Model

AI-based release decision, often referred to as AI model deployment or rollout, involves determining when and under what conditions an AI system should be put into production or made available to end-users. Here are some key considerations for making AI-based release decisions:

Model Evaluation: Before making a release decision, it’s essential to thoroughly evaluate the AI model’s performance using appropriate metrics. This evaluation should include various aspects, such as accuracy, precision, and any other relevant performance indicators. The model should meet predefined quality and accuracy standards.

Here is the AI model designed…

Based on the above, the most important decisions are arrived at, which are mentioned below:

Release Tollgate Decision

This decision entails the criteria for Production Readiness, determining whether to sign off for production or not. The decision is based on the provided values.

Quality Quotient

The Quality Quotient is a percentage derived from established metrics used for assessing and improving software quality. The following parameters are captured, and the quality quotient is determined with a predefined formula. The decision is based on the following range of values: 0% to 98%.

Testing & Validation

Extensive testing is necessary to identify and address potential issues, including edge cases that the AI model might encounter. Testing should cover a wide range of inputs to ensure the system’s robustness. Validation involves verifying that the AI model’s performance aligns with business objectives and requirements to contribute to the desired goals.

Use Cases

This model is evaluated for two projects. One is in the social media domain, which has weekly pushes to production. We have the model with the process of capturing the status of tests and defects through tools like JIRA and qTest. The captured data is fed into a dynamic dashboard with built-in formulas for calculating the metrics needed for sign-off.

The results are greatly helpful in making the release decision. We have some feedback mechanisms which helped to evolve the model and we are recommending the same to the customer.

The second one is for a fortnightly release financial domain project. Here the model gave indicative results for making the release decision.

Release decisions should be data-driven and grounded in a well-defined process that considers the AI system’s technical and business aspects. It’s crucial to strike a balance between delivering AI solutions swiftly and ensuring they adhere to quality, ethical, and security standards. Regularly reviewing and updating the release criteria is essential as the AI system evolves and new information emerges.

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Understanding OCR

OCR is a technology used to convert scanned documents or images containing text into computer-readable text, allowing automated data extraction and analysis.

Real-life Applications of OCR in Automation Testing

Extracting Data: Extract crucial information like invoice numbers from invoices, receipts, or forms. By using this, we can perform validations, ensuring that software correctly processes and stores such information.

Test Data Generation: Reads test data from legacy systems or documents and creates test scenarios and test cases, reducing manual effort in data preparation.

Example 1: Extract product details, prices, and customer information from invoices and purchase orders. This is used to perform end-to-end testing, ensuring accurate order processing and improving customer experience.

Example 2: Digitize prescriptions and medical reports which are used in automated testing of EHR systems, guaranteeing the correct storage and recovery of patient information, medications, and treatment histories.

Introduction to NLP

NLP is a branch of artificial intelligence that helps computers understand, interpret, and generate human language. Its role is to bridge the gap between human communication and machine understanding, allowing software to process, analyze, and respond to text and speech data in a way that resembles human language comprehension.

Real-Time Examples of NLP in Automation Testing

Log Analysis: Identifies patterns and errors in log data, automates the detection of exceptions, and reduces the need for physical log inspection.

Test Case Generation: Converts natural language requirements into executable test cases. By translating textual descriptions of desired functionalities, NLP streamlines test case creation, ensuring that test cases accurately reflect intended behavior and reducing the time required for test design and scripting.

Chatbot Testing: By simulating user conversations with natural language, NLP ensures the chatbot’s understanding and ability to provide appropriate responses, improving overall functionality and user experience.

Accessibility Testing: Assesses the clarity and correctness of textual content for screen readers and visually impaired users.

Localization Testing: Automatically compares source and target language content to ensure that localized versions of software or websites accurately reflect the original text and cultural requirements for various global audiences.

Integration of OCR and NLP

Combining OCR and NLP in automation testing allows for advanced capabilities, such as extracting and comprehending text from images or documents, enabling sophisticated data validation and test case generation.

Extracting Text from Images: OCR can extract text from images, making content machine-readable. NLP can then analyze the extracted text, allowing automation scripts to validate the information in image-based UI testing.

Sentiment Analysis on User Reviews: NLP can perform sentiment analysis on user reviews, categorizing opinions as positive, negative, or neutral. Combined with OCR, you can extract textual reviews from images or unstructured data sources, enabling automation to assess user sentiment without manual data entry.

Benefits of Using OCR and NLP in Automation Testing

The integration of OCR and NLP minimizes manual effort in data entry and test case generation, allowing testing teams to focus on higher-level tasks. Additionally, these technologies excel at handling complex scenarios, such as analyzing vast amounts of textual and visual data, enhancing test coverage, and overall testing effectiveness.

Conclusion

In conclusion, the synergy of OCR and NLP in automation testing promises a transformative leap in efficiency, accuracy, and coverage, ushering in a new era of software quality assurance where intricate testing challenges can be met with ease, precision, and speed.

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