Android WebView Caused a Google App Crash: How to Avoid a Similar Outage

James Smith
SmartBear
Learn more about SmartBear

On March 22, Android users around the globe suddenly saw notifications pop up on their devices saying that apps had stopped running. Critical apps such as Gmail, Google Pay, Amazon, Yahoo and certain banking apps couldn't be opened, creating widespread consumer concerns. Later, Google revealed the cause was a bug residing in the Android System WebView. Some users were able to remediate this issue by manually uninstalling the latest update and waiting for Google to release a fix. While the issue was resolved by relying on affected consumers to manually update, major crashes and painful manual workarounds can leave a lasting negative impression for users and the brand's reputation.

Software bugs are inevitable in code, so engineering teams don't realistically need to aim for 100% error-free software. However, they should have pre-production quality assurance measures in place that act as a safety net for situations like this. These tools provide comprehensive error diagnostics and actionable insights that allow software engineers to prioritize the bugs creating the most damaging user experience. Even giants like Google and Facebook still experience lapses in this process, but it is a critical step in delivering consistent, quality software.


Post-Mortem Evaluation: Breaking Down App Stability Data from the Crash

At the start of the Android app outage, Bugsnag data illustrating app stability showed four times the volume of regular Android errors registered within one day, indicating significant impact across the Android user base. The Webview bug caused approximately 75% of the crashes in the leading Android projects monitored. These projects saw around 40 times more crashes compared to the same period in the previous week. On top of that, the worst-affected projects saw 200 times the number of crashes compared to the same period in the previous week.

Additionally, an estimated 2 million users were impacted across all apps that were monitored. There was also a detected drop in overall application stability by at least 2% in Android applications, with the worst-affected projects seeing a 10% decrease in app stability scores, meaning 1 in 10 Android customers were experiencing a crash.

It's also worth noting that this Android WebView error was caused by a Native Development Kit error (NDK), which can only be detected if your crash reporting supports NDK crash detection, and if it is enabled. App stability monitoring is critical in situations like this, because certain systems don't make you opt-in for NDK monitoring like you do with others. Make sure NDK error detection is available by default.

Best Practices To Protect Your Apps from Similar Outages

Given that it was an operating system component at fault in this scenario, there is not a lot development teams could have done to prevent applications from crashing in this situation. However, there are many other types of serious app outages that can be prevented by implementing best practices and defensive programming. Below are some proactive steps engineering teams can take to protect their applications from similar problems that may impact application stability:

1. Monitor for Stability Issues in Production

This is critical for engineering teams to gain immediate visibility into crashes and spikes in errors. Not only can engineering react quickly to fix issues, but it supports impact analysis which can be used to provide clear guidance to support and customer success teams to handle customer communications with confidence. Configure team notifications and incident management integrations to quickly align the team and deal with business-critical issues.

2. Track Application Freezes

This will give the team visibility into if certain features are the root cause of any ANRs (Application Not Responding) being captured. You can track application freezes by using the stack trace to see if the line of code that was running when the application froze and set off the ANR. Stack trace information identifies where in the program the error occurs so that it can be fixed.

3. A/B Test New Features

This will help teams understand how certain features are impacting application stability before releasing them to production. You should also always phase the rollouts and test features with a small group of users before releasing to your entire user base.

The Key Takeaway

Because consumers rely heavily on mobile apps to navigate day-to-day life, application stability is absolutely critical, especially in today's relentlessly competitive environment. Difficult-to-prevent system errors like the Android Systems Webview crash highlight the importance of minimizing preventable errors with defensive programming and better handling of malformed data.

The silver lining of outages like this is that it draws attention to the dire need for good software design and process. It surfaces where software engineering teams need to introduce new best practices or where to to fine-tune existing ones.

James Smith is SVP of the Bugsnag Product Group at SmartBear

Related Links

Hot Topics

Related Vendors

The Latest

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Reliability Is the New Bottleneck of Innovation

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

5 Takeaways from the Observability Forecast for Retail and eCommerce

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Maximizing Impact Amid Constraints: The Role of Automation and Orchestration in Federal IT Modernization

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

The SRE Report 2026: Reliability Is Being Redefined

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Why AI Is the Differentiator for Operationally Resilient Organizations

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

Android WebView Caused a Google App Crash: How to Avoid a Similar Outage

James Smith
SmartBear
Learn more about SmartBear

On March 22, Android users around the globe suddenly saw notifications pop up on their devices saying that apps had stopped running. Critical apps such as Gmail, Google Pay, Amazon, Yahoo and certain banking apps couldn't be opened, creating widespread consumer concerns. Later, Google revealed the cause was a bug residing in the Android System WebView. Some users were able to remediate this issue by manually uninstalling the latest update and waiting for Google to release a fix. While the issue was resolved by relying on affected consumers to manually update, major crashes and painful manual workarounds can leave a lasting negative impression for users and the brand's reputation.

Software bugs are inevitable in code, so engineering teams don't realistically need to aim for 100% error-free software. However, they should have pre-production quality assurance measures in place that act as a safety net for situations like this. These tools provide comprehensive error diagnostics and actionable insights that allow software engineers to prioritize the bugs creating the most damaging user experience. Even giants like Google and Facebook still experience lapses in this process, but it is a critical step in delivering consistent, quality software.


Post-Mortem Evaluation: Breaking Down App Stability Data from the Crash

At the start of the Android app outage, Bugsnag data illustrating app stability showed four times the volume of regular Android errors registered within one day, indicating significant impact across the Android user base. The Webview bug caused approximately 75% of the crashes in the leading Android projects monitored. These projects saw around 40 times more crashes compared to the same period in the previous week. On top of that, the worst-affected projects saw 200 times the number of crashes compared to the same period in the previous week.

Additionally, an estimated 2 million users were impacted across all apps that were monitored. There was also a detected drop in overall application stability by at least 2% in Android applications, with the worst-affected projects seeing a 10% decrease in app stability scores, meaning 1 in 10 Android customers were experiencing a crash.

It's also worth noting that this Android WebView error was caused by a Native Development Kit error (NDK), which can only be detected if your crash reporting supports NDK crash detection, and if it is enabled. App stability monitoring is critical in situations like this, because certain systems don't make you opt-in for NDK monitoring like you do with others. Make sure NDK error detection is available by default.

Best Practices To Protect Your Apps from Similar Outages

Given that it was an operating system component at fault in this scenario, there is not a lot development teams could have done to prevent applications from crashing in this situation. However, there are many other types of serious app outages that can be prevented by implementing best practices and defensive programming. Below are some proactive steps engineering teams can take to protect their applications from similar problems that may impact application stability:

1. Monitor for Stability Issues in Production

This is critical for engineering teams to gain immediate visibility into crashes and spikes in errors. Not only can engineering react quickly to fix issues, but it supports impact analysis which can be used to provide clear guidance to support and customer success teams to handle customer communications with confidence. Configure team notifications and incident management integrations to quickly align the team and deal with business-critical issues.

2. Track Application Freezes

This will give the team visibility into if certain features are the root cause of any ANRs (Application Not Responding) being captured. You can track application freezes by using the stack trace to see if the line of code that was running when the application froze and set off the ANR. Stack trace information identifies where in the program the error occurs so that it can be fixed.

3. A/B Test New Features

This will help teams understand how certain features are impacting application stability before releasing them to production. You should also always phase the rollouts and test features with a small group of users before releasing to your entire user base.

The Key Takeaway

Because consumers rely heavily on mobile apps to navigate day-to-day life, application stability is absolutely critical, especially in today's relentlessly competitive environment. Difficult-to-prevent system errors like the Android Systems Webview crash highlight the importance of minimizing preventable errors with defensive programming and better handling of malformed data.

The silver lining of outages like this is that it draws attention to the dire need for good software design and process. It surfaces where software engineering teams need to introduce new best practices or where to to fine-tune existing ones.

James Smith is SVP of the Bugsnag Product Group at SmartBear

Related Links

Hot Topics

Related Vendors

The Latest

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Reliability Is the New Bottleneck of Innovation

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

5 Takeaways from the Observability Forecast for Retail and eCommerce

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Maximizing Impact Amid Constraints: The Role of Automation and Orchestration in Federal IT Modernization

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

The SRE Report 2026: Reliability Is Being Redefined

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Why AI Is the Differentiator for Operationally Resilient Organizations

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...