Quality Control Using Artificial Intelligence

Creating software is never a straightforward process and requires rigorous quality assurance (QA) to guarantee its quality and usability. Failing to do so could result in the delivery of faulty or unusable software that does not meet the specific requirements of customers, employees, or other external parties.

Quality Assurance (QA) and QA outsourcing are now essential elements of creating any modern software. In order for this to be successful, the design, development, testing and release phases must be carried out in the correct order. To guarantee that the product will always respond as intended, QA engineers must work throughout the entire software development life cycle, making use of agile methodologies and testing every advancement in small and incremental steps.

It is expected that a Quality Assurance (QA) strategy would be implemented in an Artificial Intelligence (AI) development project. Nonetheless, this is not always the case. The traditional four-stage iterative process remains largely unchanged, however AI-driven activities may be quickly deployed due to the dynamic nature of AI, as it is always learning and adapting, and thus requires regular monitoring.

As a result, quality assurance (QA) for AI projects is different from QA for non-AI applications. This is why.

Meaningful Quality Assurance and Testing for Artificial Intelligence

Testing is an essential part of developing Artificial Intelligence (AI). Simply providing an algorithm with training data is not enough to create successful AI solutions. Quality Assurance (QA) and testing is used to ensure that training data is effective and able to perform the required tasks.

How can we achieve this? We can do so by employing fundamental methods of verification. Essentially, AI QA engineers must select which elements of training data to utilise in the validation process. The AI is then evaluated in a meticulously devised scenario to assess its capability in making forecasts and how efficiently it processes data.

In the event that the Quality Assurance (QA) team identifies serious issues during validation, the Artificial Intelligence (AI) must be returned to the development phase, just like any other software development project. Upon completion of the required refinements, the AI must be re-submitted to QA to ensure it is providing the expected results.

The QA team are yet to complete their work as AI models require additional testing, with the timeframe dependent on the resources and time available. Prior to releasing a production version, the QA engineers must repeat the steps outlined above repeatedly.

The development team conducts a series of tests on the algorithm, often referred to as the ‘training phase’ of Artificial Intelligence (AI). Quality assurance, however, does not examine the code or AI algorithm itself; rather, they assume that it has been correctly implemented before verifying whether the AI performs as expected.

QA Specialists will predominantly use hyperparameter configuration data and training data to carry out quality assurance. Validation techniques, such as cross-validation, are regularly employed to assess the hyperparameter settings. Verifying these settings is a vital component of any AI research endeavour, without doubt.

The next stage is to test the training data. Quality assurance engineers must do more than simply assess the accuracy of the data; they must also ensure that all the required fields have been completed. These are all excellent starting points.

  • Is the algorithm’s training model constructed to faithfully reflect the world the algorithm will attempt to predict?
  • Is there any possibility that the training data is being skewed by either data-based or human-based biases?
  • Is the algorithm missing anything that would explain why it performs well during training but not in the actual world?

As the project progresses, it is likely that further questions about quality assurance will arise. The QA team should have access to representative samples of real-world data, as well as an understanding of AI bias and its implications for AI ethics, in order to be able to respond to these queries effectively.

There must be production-level AI testing.

It is essential that Quality Assurance staff have a thorough understanding of when Artificial Intelligence software has been adequately tested, when the training data is sufficient, and when the algorithm has been proven to provide reliable results.

It is widely accepted that data is ever-changing and growing, so it is important to have a Quality Assurance (QA) strategy in place for Artificial Intelligence (AI) development which can be applied during the manufacturing phase.

Assuming approval has been granted, Quality Assurance will initiate a new cycle of assessing the AI’s performance and behaviour when exposed to new real-world data. It is of paramount importance to closely monitor the evolution of any AI project, no matter its scope or complexity. A robust Quality Assurance process is the best approach to achieve this.

Machine Learning Operations (ML Ops) has become a widely accepted term. Quality Assurance Engineers are responsible for managing the entire lifecycle of an AI system, from version control to software management, cybersecurity, iterative procedures, and discovery phases. We hope this essay has provided you with a better understanding of Quality Assurance and Artificial Intelligence. We wish you every success.

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