![]() Once all requests are verified by the QEs, the release is automatically approved. We now have a system that regularly sends Slack alerts to QEs to update them on the verification status of a pull request before the release goes out to customers. Implementing the system was a great win for the release process at Airtable, saving the team some 45 hours per week.īut the team decided to take our automation a step further. It even assigns tasks if they come in after regular business hours or over the weekend. Our fully automated system can assign tasks to quality engineers around the clock-which is helpful for a remote team working in disparate timezones. Airtable automation also lets us modify records in the base after looking up the relevant QE for that pull request. Viewing that information in Airtable gives us a picture of the working relationships and connections. Our teams use a rolling 60-day window to pull information on these interactions (it wouldn’t be that relevant to track which QEs were verifying pull requests from engineers a year ago). Since we have historical records of all pull requests verified by QEs, we can run an Airtable script action to automatically look up all instances in which an engineer has had a corresponding QE verify their change. We wanted to leverage the rich history and existing data in our Airtable base, which creates a record each time a QE gets matched up to verify the pull request from a specific engineer. Another wrench in the plan: with new engineers joining the team each week, someone had to manually keep the table up to date. This would require quite an effort to chart, since there are hundreds of engineers at the company and only a handful of QEs. In other words, we needed to create a table that showed those engineer-QE connections. We needed to codify and automate a process that previously only existed in one person’s head. Work would come to a screeching halt any time the Quality Analyst was out, and no one else had a grasp on the engineer-QE connections. With new people joining the company every week, the process quickly became unscalable. Keeping up with these match-ups was a full-time job. The QE would then validate changes the engineer made in the code. ![]() ![]() In the past, assigning tasks meant a Quality Analyst with knowledge of both the engineering team and the QE team matched up an engineer with a corresponding QE. It also shows which are ready for testing. In that base, the team tracks which pull requests have been merged to the main branch, a central repository. The QE group uses an Airtable base to track things like pull requests, or small changes to the software. Examples of testing tasks include finding bugs or anomalies in our product, ensuring the product is compliant with functional and non-functional requirements, and getting bugs resolved by developers. Project 1: Automating task assignmentĭuring the SDLC, after engineers complete writing code, the quality engineers on the team are assigned to test code changes. Once testing is complete, the new version of the code is released to our customers. Our engineering teams use the agile development method, with two weeks of cross-functional team members working and iterating on developing and testing code. ![]() One of the use cases is to manage our software product development lifecycle (SDLC). Our group uses Airtable for several internal workflows. We are a remote-first team, meeting on a quarterly basis in Airtable’s Mountain View office for in-person collaboration and team bonding. The Quality Engineering group is close to 27 engineers working from various parts of the U.S. So we made an effort to automate those manual steps, which resulted in a dramatic streamlining of task assignments and a savings of 70% in Quality Engineering team bandwidth for every new software release. The Quality Engineering Team at Airtable.īut a few months back, we realized that our process involved many manual steps. ![]()
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