Use of Cognitive QA to improve engineering and operations

In my last blog post, I introduced the topic of Cognitive QA and how we enable smart, quality decision-making based on factual project data, actual usage patterns, and user feedback. In this blog post, I’ll be discussing some of the use cases wherein applying analytics has helped organizations make a significant impact to improve engineering and operations and the road ahead.

In recent years, we have helped many product engineering teams to implement the capability of leveraging analytics in their engineering operations. Some of the use cases our team worked on include:

Product management function

We are currently working with a few customers in the regulated medical devices, and aerospace and defense industries, where the focus of applying analytics is mainly to determine the typical features that are frequently used, the usage patterns of the products, configurations, etc. We worked with a medical device customer recently who wanted us to look at the clinical logs generated while using their product and compare it with the logs generated through automation and identify frequently used features and workflows.

Development function

We’ve been working with a leading global beverage manufacturer, analyzing their source code to help them derive hotspots and identify the context-specific tests they need to execute. We have also been working with a medical device manufacturer in proof of value to automate the process of impact analysis. The regulated medical devices industry is focused on impact analysis and using the power of analytics helps them get it done faster.

Quality assurance

When it comes to the QA use cases, we have been working with Dell EMC. For more details on our experience, please take a look at this. We are also currently working with a medical device manufacturer in identifying duplicate tests scenarios, defect deduplication, etc.

Release management

We are currently engaged with a European automotive OEM’s central release management team, working on helping the team to take a go-no-go decision, predicting the risk level of various product components that are received as part of their software supply chain.

Where do we apply analytics in our operations?

Capgemini has been working with several product engineering companies to apply analytics to improve operations. In fact, we have an entire engineering analytics competency team that focuses on the operational aspects of applying analytics.

One common concern among customers is how to identify the low-hanging fruits to apply analytics. In other words, what are the opportunities and the areas to use analytics in our operations?

We already have various mechanisms to improve the way we work, such as heuristics, a rule of thumb, best practices, common sense, insights, intuitive judgments, feedback, and SME tribal knowledge. A primary goal of applying analytics is to help us “automate” or “codify” these “heuristics” and “rules” for better and faster decision making.

Let’s take an example from the quality domain to understand how we “automate” or “codify” rules. In the case of a test selection problem, we use common sense to test defects which have been fixed, identify related test failures, and pick up those tests. We then identify dependent modules from the code base and identify tests for those modules, identify prior failed tests when a similar code change happened, etc.

Though all of the above can be done manually, in a continuous engineering organization it is very difficult to get these done effectively without automation. This is where we can leverage the power of analytics and automate or codify the rules.

In my next blog of this series, I will discuss the rules of applying analytics to our operations, the challenges and key takeaways.