From bugs to performance to perfection: pushing code quality in mobile apps

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Dec 6, 2024

Kenny Johnston, Chief Product Officer at Instabug, focuses on enhancing mobile app quality with AI tools. He discusses the challenges of SDK bloat and how Instabug has engineered a lean SDK to minimize performance impact. Kenny also shares insights into the future of self-fixing code in mobile development, emphasizing the potential for auto-healing solutions. He highlights the importance of harnessing AI to improve crash diagnosis and meet evolving user expectations in software, ultimately transforming mobile development into a more efficient process.

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ADVICE

Effective Bug Reporting

Capture extensive device and environmental information during bug reports.
Store the information efficiently to minimize performance impact, especially on older or low-end devices.

INSIGHT

Mobile Debugging Complexity

Mobile app debugging is complex due to diverse device environments (age, hardware).
Consider the environment's impact, such as battery state or network connectivity, during debugging.

ADVICE

SDK Optimization

Minimize SDK performance impact on mobile apps, especially on lower-end devices.
Optimize data capturing and offloading to storage to reduce network usage and resource consumption.

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Instabug helps developers monitor, prioritize, and debug performance and stability issues throughout the mobile app development lifecycle. Get started with their docs.

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Some great excerpts from today’s episode:

On why they built a lean, mean SDK: “Nowadays mobile developers spend a lot of time thinking about SDK bloat and how much they're taxing their app’s performance just from the SDKs they’re including. We spent a lot of time and a lot of effort making sure that our SDK has very minimal performance impact. You can't do this without any performance impact, but making sure that it has really minimal performance impact as an SDK itself. A lot of that has to do with the way in which, from years of experience, we capture the information and offload certain information to storage for when we have network connectivity bandwidth later so that we're not constantly eating network.”

On the future of self-fixing code and mobile app development: “Our belief is that the place where we're going to see this kind of auto fixing of code, auto healing of code, it's probably going to be mobile first. So we're invested heavily in seeing that reality. You can think of it as straightforward as crashes, for example. There's a known set of crash error codes. And so there's a known set of crash behaviors. So it's pretty easy for us. And that was what our smart resolve 1.0 was to get to, Hey, this is generally how you should solve these types of crashes. Our 1.0 version is not giving you code suggestions, but it's at least giving you known best practices from places like Stack Overflow and others that have content about how to solve these types of problems.”

On using AI models to spot UI issues: “We think that there are a lot less deterministic ways to spot a frustration signal. So the thing we're working on is, on device models for your users’ behavior that will allow our SDK to capture a frustration signal that nobody else has. Maybe today when I opened my banking app, I usually look at page one and then do a transfer, check out my balance, and now I'm doing this weird swiping behavior because something's not working well. A model could spot that. It wouldn't be reported as a bug, but a model could spot that.”