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Quality Assurance: Guide to automating QA with AI in 2024

Updated on Feb 13
3 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Quality assurance (QA) plays a critical role in software development. A good QA process protects a company’s reputation and keeps its development costs low and customer satisfaction high. However, numerous time-intensive and manual QA tasks slow down QA teams and keep QA costs high. Artificial intelligence is enabling autonomous tools for time-consuming QA tasks.

What is quality assurance?

Quality Assurance is a set of methods and activities to ensure that software meets expected specifications or standards. These predefined standards can be one or a combination of ISO 9000, CMMI model, ISO15504, etc. The main aim of QA is to find ways to prevent possible bugs in the software development process through comprehensive testing. Bugs that are found and fixed early are less costly.

Quality assurance focuses on five quality characteristics:

  • Functionality: Examines the functionality of the software, potential security vulnerabilities and integration issues
  • Reliability: Software’s ability to continue operating correctly and consistently under challenging conditions
  • Usability: Ease-of-use of the software
  • Efficiency: The quality assurance teams need to make sure that architecture and coding practices of the software are well designed. The whole software system should be able to operate under the resource constraints it can face in production environments.
  • Maintainability: Ease of maintenance, taking into account how future proof the software architecture is.

Why is Quality Assurance important now?

Leading companies in every industry from Goldman Sachs to Walmart claim to be technology companies. Today’s technology from biotech to nano-tech is inseparable from software and software quality is dependent on QA. Companies in all industries need to pay attention to their QA process and ensure that they have a best practice software development process.

What are the benefits of QA?

An effective QA process

  • improves customer satisfaction and protects a company’s reputation
  • reduces software development cost as early discovery of bugs reduces debugging efforts

How is AI changing QA?

Companies can not afford to hold back releases due to QA. Releasing software fast enables companies to fix bugs faster and make customers happier. Machine learning techniques are being used to reduce the time consuming aspects of QA to enable faster releases.

For example, during the testing process with artificial intelligence, changes on the software can be monitored and proper tests can be suggested by the AI powered QA tools. This results with less time spent to select test methods, saving developer time.


PULSE is an example of an automated AI-based testing tool designed for API testing by Testifi. PULSE reduces cost and effort by +50% in performance monitoring and improving product release and documentation. Global companies such as Amazon and BMW use their services.

Test selection & design

A Facebook QA team has developed a method based on machine learning aims to select regression tests for code changes. This method develops an automated test selection strategy by reviewing code changes and running small test subsets. Model learns from the results, uncovering bugs faster over time.

Netflix has developed a library called Lerner that plans test scenarios using a series of micro services and scalable agents. A code change may trigger hundreds of time consuming test scenarios. Lerner, using a reinforcement learning approach, suggests tests cases to prioritize which leads faster discovery of bugs, dramatically reducing overall test time.

Infosys Pandit is another software example that relies on machine learning to optimize testing scenarios and automate testing.

Lerner reduces test time by prioritizing test cases
Source: Netflix

Detection of GUI defects

RPA and ITPA techniques and tools are used to automate traditional testing activities. For example, bots are built to automate user interface tests of new devices (e.g. smartwatches, smartphones), new web and mobile applications. These bots ensure that the consistency of the UI is continuously and automatically checked.

A research group in eBay has developed a faster and more effective method than traditional automated and manual tests using convolutional neural networks. This team used ML to detect abstract components such as images, shapes, text and extract these components and their respective positions. This helped to identify the GUI defects across multiple operating systems, devices, screen resolutions, and browsers. 

If you have questions about how Quality Assurance is important for your business, we can help:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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