In a perfect world, implementing DevOps means that every person has the necessary information to accurately prioritize their work, complete their tasks, and collaborate with the rest of the development pipeline. Code smoothly transitions from development to testing to production, with each stakeholder updated on its progress along the way. No one wastes valuable time tracking down status updates, context, or trying to hand off projects between teams.
Unfortunately, that’s often not the case. As more teams start their DevOps journey, they start finding new ways to extract information from the software development lifecycle without a plan to make the data useful. As a result, aspiring DevOps teams are inundated with new data points and struggle to make their workflows more efficient, which contributes to a lack of sustainable change along the road to DevOps.
Data-Driven Collaboration Drives Change
Quality engineering is in an excellent position to lead collaboration across the DevOps pipeline simply because everyone has a stake in ensuring that the product works. When used effectively, test data guides and affirms the entire team’s role in building better software, which makes it easier to break down siloed workflows for the long term.
Accelerate Bug Fixes with Context
Over one-third of developers say they struggle to quickly address bugs because they need to search for the necessary information. On one hand, this means that many teams are struggling to manage collaboration between developers and QE teams, making it difficult to quickly resolve bugs. On the other hand, it means that repairing communication in just one interaction - the hand off process - would eliminate a large number of delays in the development process.
Modern test automation platforms make the hand off process easier by automatically including context around defects within issue tickets in the form of DOM snapshots, screenshots, and diagnostic data.
Creating a Jira ticket in mabl
When test data can be directly added to Jira tickets, quality teams and developers can easily create shared workflows that reduce friction and prevent vital information from being lost in transition between multiple colleagues.
When test data is used to provide context to bugs, it’s far more likely that teams can address issues faster, so it’s no surprise that mabl’s State of Testing in DevOps Report found a strong correlation between testing and fast bug resolution.
Align Code and Customers
Prior to DevOps, many companies developed software in a linear fashion, cutting off feedback once code was deployed into production. Over time, this limitation created diverging goals between engineers, quality teams, and executives. Engineers kept their focus on delivering code as quickly as possible, while quality teams are driven by ensuring a flawless user experience. Executives are more likely to be watching metrics like growth and customer retention. Quality engineering backed by effective testing has emerged as the common thread between all three goals.
The most obvious instance is that fewer bugs in production equals a better customer experience. But even before code is user-facing, software testing can be used to create a connection between the people writing the code and their end-users by removing assumptions about user behavior. When automated testing solutions are integrated with customer data platforms like Segment, DevOps teams can build software with real user journeys in mind, allowing developers and QE specialists to prioritize new features and testing based on how their audience interacts with their applications.
Create Focus with Data-Driven Feedback
Everyone understands the struggle of prioritizing projects when inundated with notifications, messages, and information from across the organization. Though DevOps seeks to build better software through collaboration, automation, and data, the introduction of new tools designed to harvest information without a plan to use it only results in data overload. Quality engineering teams can overcome this by keeping the organization focused on quality with test data, especially when supported by test automation platforms that allow test results to be easily integrated into existing DevOps workflows.
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