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

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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

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AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

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Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

The Network Data Problem Slowing Down Enterprise AIOps

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

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Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

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