How to Manage Mobile App Development Complexity with Modern APM

Dave Hayes
Sentry

With a projected 7 billion mobile users by 2021, mobile is becoming the most dominant digital touchpoint for customer engagement. Annual mobile app downloads are projected to reach 258 billion by 2022 — a 45% increase from 2017. But downloads alone do not indicate mobile success — retention and engagement are key. While there are many factors that influence these metrics, application performance may be one of the most critical.

Application crashes can increase churn and damage brand reputation. In fact, app crashes cause more than 70% of uninstalls. Because Google ranking algorithms now downrank apps with stability problems, uptime and performance can even influence download metrics. It is imperative that organizations are delivering high-performing mobile applications and that the developers supporting those applications have the ability to identify and remediate errors efficiently.

But due to the growing complexity of the application ecosystem, this can be easier said than done. Developers are often limited by a lack of visibility and control over mobile devices. They have no way of knowing every device’s attributes or predicting which other apps and system processes may be competing for compute power. Mobile app developers are no strangers to segmentation faults and bus errors. And programming for such a diverse array of devices creates its own set of challenges. Flaws in native code, third-party dependencies or, in rare cases, even in the system libraries, can bring down the entire application.

Developers today are under pressure to deliver code faster than ever before. Even when rigorous testing processes are in place, the increasing complexity of application development will inevitably cause errors. Monitoring is an important component of application delivery, but the changing ecosystem calls for the reinvention of application performance monitoring that incorporates rich context and actionable insights.

Rethinking Application Performance Monitoring

While companies with faulty code are dealing with the fallout, those that rely on modern application performance monitoring (APM) are shipping better code more quickly and with less risk. Legacy monitoring tools focus more on system behavior, offering performance metrics on availability, throughput, and latency, but miss the mark when it comes to actionable insight that helps mobile developers make sense of complexity and quickly get to the root cause of errors.

On mobile — as with all native applications — it is important to have context beyond a crash. Developers need rich details supporting the error, such as the types of phones impacted, the number of users impacted, the specific actions a user took when the error was thrown, or even the storage capacity and battery life of the user's phone at the time the app crashed. These details are especially important for mobile developers because of the vast array of devices and native libraries used in programming. Having this information enables them to immediately triage and prioritize problems.

When developers can also identify the exact release and commit the error is tied to, they can quickly remediate the issue to minimize the breadth of impact. Developers can also move this feedback into the development cycle. By capturing every single exception and crash users encounter, meaningful trends will surface to help prioritize issues and avoid replication in future software release.

It is also important to consider that modern applications are not self-contained — they have multiple runtimes across the stack, causing added complexity in monitoring. Support for mobile, coupled with similar support for web, gives developers a complete picture, which is key in today’s application-centric landscape.

Innovation in the mobile space shows no signs of slowing, so developers must take proactive steps to address the factors that derail mobile app development and wreak havoc on user experience. By taking a modern approach to APM, incorporating rich context and actionable insights, and syncing this information across all of their applications, developers can code better and faster, ensuring their companies remain successful and competitive in the mobile world.

Dave Hayes is Head of Product at Sentry

Related Links

Hot Topics

The Latest

Observability Is the New Control Plane for Enterprise Transformation

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

When AI Becomes the Corporate OS

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

How to Manage Mobile App Development Complexity with Modern APM

Dave Hayes
Sentry

With a projected 7 billion mobile users by 2021, mobile is becoming the most dominant digital touchpoint for customer engagement. Annual mobile app downloads are projected to reach 258 billion by 2022 — a 45% increase from 2017. But downloads alone do not indicate mobile success — retention and engagement are key. While there are many factors that influence these metrics, application performance may be one of the most critical.

Application crashes can increase churn and damage brand reputation. In fact, app crashes cause more than 70% of uninstalls. Because Google ranking algorithms now downrank apps with stability problems, uptime and performance can even influence download metrics. It is imperative that organizations are delivering high-performing mobile applications and that the developers supporting those applications have the ability to identify and remediate errors efficiently.

But due to the growing complexity of the application ecosystem, this can be easier said than done. Developers are often limited by a lack of visibility and control over mobile devices. They have no way of knowing every device’s attributes or predicting which other apps and system processes may be competing for compute power. Mobile app developers are no strangers to segmentation faults and bus errors. And programming for such a diverse array of devices creates its own set of challenges. Flaws in native code, third-party dependencies or, in rare cases, even in the system libraries, can bring down the entire application.

Developers today are under pressure to deliver code faster than ever before. Even when rigorous testing processes are in place, the increasing complexity of application development will inevitably cause errors. Monitoring is an important component of application delivery, but the changing ecosystem calls for the reinvention of application performance monitoring that incorporates rich context and actionable insights.

Rethinking Application Performance Monitoring

While companies with faulty code are dealing with the fallout, those that rely on modern application performance monitoring (APM) are shipping better code more quickly and with less risk. Legacy monitoring tools focus more on system behavior, offering performance metrics on availability, throughput, and latency, but miss the mark when it comes to actionable insight that helps mobile developers make sense of complexity and quickly get to the root cause of errors.

On mobile — as with all native applications — it is important to have context beyond a crash. Developers need rich details supporting the error, such as the types of phones impacted, the number of users impacted, the specific actions a user took when the error was thrown, or even the storage capacity and battery life of the user's phone at the time the app crashed. These details are especially important for mobile developers because of the vast array of devices and native libraries used in programming. Having this information enables them to immediately triage and prioritize problems.

When developers can also identify the exact release and commit the error is tied to, they can quickly remediate the issue to minimize the breadth of impact. Developers can also move this feedback into the development cycle. By capturing every single exception and crash users encounter, meaningful trends will surface to help prioritize issues and avoid replication in future software release.

It is also important to consider that modern applications are not self-contained — they have multiple runtimes across the stack, causing added complexity in monitoring. Support for mobile, coupled with similar support for web, gives developers a complete picture, which is key in today’s application-centric landscape.

Innovation in the mobile space shows no signs of slowing, so developers must take proactive steps to address the factors that derail mobile app development and wreak havoc on user experience. By taking a modern approach to APM, incorporating rich context and actionable insights, and syncing this information across all of their applications, developers can code better and faster, ensuring their companies remain successful and competitive in the mobile world.

Dave Hayes is Head of Product at Sentry

Related Links

Hot Topics

The Latest

Observability Is the New Control Plane for Enterprise Transformation

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

When AI Becomes the Corporate OS

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...