Deep Dive into iOS Automation at Grab - Continuous Delivery

Summary

Grab manages continuous delivery for its iOS applications using four build configurations per target: Adhoc QA, Hot Dogfood, Dogfood, and Testflight. The engineering team moved away from Fastlane in favor of custom bash scripts under 100 lines that execute clean builds and archives via xcodebuild. To eliminate manual logins for build execution, Grab developed a server-side Swift application integrating SlackKit and a bot named Iris. Incoming Slack commands are parsed and scheduled onto a serial DispatchQueue to trigger the appropriate build scripts. Deployment and rollback of updates to Iris and its build scripts are handled through Capistrano.

Context

To bypass the device whitelist limits of standard Apple developer accounts, Grab needed an internal beta app distribution system and a way to trigger builds without requiring manual logins to build machines.

Approach / What changed

Grab implemented custom bash scripts around xcodebuild, created a server-side Swift Slackbot named Iris using SlackKit to queue archive jobs, and utilized Capistrano to manage Iris deployments.

Takeaways

• Grab replaced Fastlane with concise, sub-100-line bash scripts using xcodebuild to reduce third-party dependencies and gain build flexibility.
• The Iris Slackbot enqueues archive tasks onto a serialized DispatchQueue to run shell scripts and post status updates back to Slack.
• Capistrano automates deployments and rollbacks for the Iris server application, linking shared configuration files across releases.

Related reading

• Grab · Apr 18, 2017

  Deep Dive into iOS Automation at Grab - Integration Testing

  Grab replaced third-party cloud CI services with an in-house Xcode Server pipeline to run automated iOS integration tests across an expanding engineering team. Instead of performing fresh repository clones on shared cloud instances, Xcode Server pulls code incrementally and mirrors local developer configurations. The team automated testing bots through Ruby scripts and Arcanist hooks tied to Phabricator code reviews, dynamically provisioning bots on diffs and deleting them after merges. This architecture preserves step-by-step UI test execution logs and screenshots for easier failure diagnostics. To maintain stability on on-premises hardware, pre-integration scripts clear application cache states using simctl commands.

  Sun Xiangxin

• Grab · Sep 23, 2020

  Our Journey to Continuous Delivery at Grab (Part 1)

  Around the end of 2018, Grab's backend architecture consisted of roughly 270 services managed through fragmented, manual deployment workflows. Engineers copied release parameters between build logs, wiki pages, Slack bots, and multiple Jenkins jobs, leading to high operational friction and an average of 10 business days between production updates for a service. To streamline delivery, Grab built Conveyor, an internal automation platform built on top of open-source Spinnaker. Conveyor introduced a custom user interface focused on pipeline visibility and a pipeline-as-code DSL called Artificer using Jsonnet files in the monorepository. The platform automatically registers build artifacts with commit metadata to eliminate manual parameter entry and automatically provisions integration, staging, and production pipelines.

  Sylvain Bougerel

• Grab · Jul 3, 2019

  Making Grab’s Everyday App Super

  Grab manages an expanding superapp ecosystem comprising ride-hailing, food delivery, payments, and partner content surfaced through the Grab Feed. As content volume grows, the platform risks overwhelming users with irrelevant information. To address this, Grab built a recommendation engine that ranks cards using signals across user profiles, content metadata, and contextual factors such as time and location. The system employs multiple recommendation strategies—including popularity metrics, user favorites, collaborative filtering, habitual patterns, and cross-platform deep embeddings—which are selected or aggregated. Recommendation quality is evaluated via offline metrics like Recall@K and NDCG alongside online engagement experiments.

  Justin Bolilia

• Grab · Mar 1, 2018

  How Grab Experimented with Chat to Drive Down Booking Cancellations

  Post-allocation ride cancellations at Grab degrade the booking experience and create costly inefficiencies for both passengers and driver-partners. Internal user research and platform data confirmed that rides involving GrabChat conversations had significantly lower cancellation rates by reducing perceived wait times. To scale this interaction without extra cost, the team tested system-generated automated messages sent at varying delay intervals, styles, tones, and localized verbiage across different cities. Faster message delivery outperformed longer delays, and tailored prompts reduced booking cancellations by up to two percentage points across tested markets. The experiment demonstrated that high-quality, directed prompts solicited quick responses and improved pick-up efficiency even when overall message volume was lower than control groups.

  Ishita Parbat