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AI-Driven

How AI-Driven Quality Engineering Is Driving Value Across The Software Development Life Cycle

AI-Driven

Day by day, artificial intelligence (AI) and machine learning (ML) are becoming increasingly popular with their use cases and applications spreading across all industries. All thanks to the organizations in various industries that are rapidly adopting applications of these emerging technologies. Businesses are now realizing the true power and significant value that can be obtained from implementing these technologies. But what is the main reason for such increased popularity of these technologies and how they are benefiting businesses?
Let’s take a close look at few use cases of AI and ML across several industries to understand their common characteristics and finally analyse them through the perspective of quality engineering.

  • AI in Travel Industry

The travel and hospitality domain is one of the industries that adopted AI/ML techniques in their early stages. To provide the customers with greater business value, organizations originating from this industry are heavily using AI/ML. These technologies act as a powerful tool to enhance the customer experience as they provide highly personalized recommendations around optimal pricing for flights, hotels, rental cars, and holidays.
They take into consideration the buying patterns, behaviours, and preferences of the customers. AI/ML algorithms leverage highly diverse and complex data sets, which allows them to make such personalized recommendations that help achieve business goals.

  • AI in Financial Services

Putting the right price for the policies as per their features and offerings, is one of the biggest challenges that insurance companies are facings. For instance, if you want to take auto insurance, you will notice that the policy rates are typically determined by simple factors, such as vehicle model, manufacturing year, etc. But the same method doesn’t factor in individual behaviours, such as driving habits, location, and weather that can greatly impact the accuracy of the rates.
Getting these data points while analysing them, manually or through traditional tools, is not only cumbersome but error-prone too. Al algorithms can help financial service providers in this context by parsing complex data, detecting anomalies, and extracting patterns of driving behaviour. All of this data can be used to build recommendations for individualized policy rates. Not only this, but you can use AI to evaluate a variety of factors that can help assess the risk of an accident.

  • AI in Media and Entertainment

A majority of organizations across the globe are now training ML algorithms so that they can develop film trailers and design advertisements. Another such application of AI in the media and entertainment industry is that it helps in recommending personalized content as per the user activities. Media content service providers are also harnessing the power of AI software to improve the speed and efficiency of their media production processes while improving their ability to organize visual assets.

  • AI in Retail

Retail business owners have their eyes on AI and how it can make highly personalized product recommendations. It collects customer data from various sources, such as website homepages, searches, kiosks, and shopping malls, and finally uses it to provide useful and smart recommendations that favour the business.
AI can also be used in retail to create market segments across types of customers, patterns in customer’s buying behaviour, coming from previous purchases, demographics, and preferences. Such segments are then used to develop personalised recommendations delivered through customer accounts on websites, email campaigns, call centre agents, and location-based advertisements across multiple channels.

AI-Driven Quality Engineering

All the industries have a different set of customers they cater to, with different needs and varying expectations. The use cases we discussed above are quite distinct and target specific industry customers. But since they lay their foundation on the same technology, we can still analyse the common characteristics across these cases for quality engineering across domains. Take a look here:

  • Extensive Data

AI algorithms collect huge volumes of customer data that is stored in multiple repositories, which can be analysed to provide the customers with useful insights in terms of their preferences, buying patterns, and behaviours. Later, the same insights can be used as inputs for application development that are particularly aimed at delivering greater business value while meeting all the expectations of the customers.

  • One Technology, Multiple Applications

These domains have a complex mesh of applications, devices, systems, and interfaces that work in tandem so that they can deliver the right business value to the customers. This intertwined mesh of “things” can add to the complexity of application development, delivery, deployment, and support. And as systems become more complex with increasing customer demands, the pressure on software delivery increases automatically.
This problem has led to the creation of nimble architecture and the adoption of Agile/DevOps on the one hand. On the other, we have the accumulation of undeveloped product features and defect backlogs, adding to technical debt. At the end of the day, it affects how applications are tested. But AI can be used to determine the priority and hot spots for testing.
Here is how AI-driven quality engineering delivers value to the software development life cycle:

  • Helps in forecasting customer requirements while dynamically tuning the test strategy.
  • Scans the use cases, applications, and test case repository so that relevant test scenarios can be included.
  • Drives extreme automation by identifying scenarios from UI, services, and API levels that can be automated.
  • Helps you develop a defect prediction model as per the criticality and previous downtimes of applications.
  •  Validates features to enhance performance, security, and stability.

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