Mobile App Bug Fixing & Performance

An app with a four star average and a review that says "crashes when I open the camera" loses installs one review at a time. The crash dashboard shows a stack trace and a device list, and none of it explains why the same build runs fine on your phone and dies on a three year old mid-range Android model.

We take over that work: crashes, UI bugs, and performance complaints that leave no stack trace at all. Launch time, stuttering scroll, memory the app never gives back, battery drain users notice by lunchtime, and the release habits that decide whether a fix reaches anyone.

What the work covers

Crashes, hangs, and UI bugs

Not every failure arrives as a crash. Some are deadlocks that freeze the interface, some are layout breaks on one screen width, and some are a button that does nothing after a permission is denied. We group reports by root cause rather than by symptom, so one fix closes dozens of reports, and we check it on the device families where the problem appeared.

Launch time and startup cost

Cold start, where the process is created from nothing, and warm start, where the app is already in memory, need different fixes. Cold start work defers anything the first screen does not need. Warm start work restores state instead of rebuilding it.

Frame drops and responsiveness

We profile rendering during the interactions users actually perform: long lists, galleries, maps, and camera previews. Dropped frames come from main thread work, oversized images, or layout that recalculates on every scroll tick. Each has a direct fix.

Memory, battery, and network

Leaked listeners, retained contexts, and background timers show up as memory growth, battery drain, and disconnections on cellular. We measure with platform profilers, fix the retention, and harden the network layer against timeouts and network handovers.

Where the project starts

The plan changes with what already exists.

A plain idea has no bugs to fix yet, so the useful work is building it well the first time, with the right startup path, memory discipline, and crash reporting in place. That belongs on our mobile app development page.

An existing conventional codebase is the main case here. We audit it, rank the failures by user impact, and fix them in short release cycles while the app keeps shipping to the store.

An AI or vibe-coded prototype is a different engagement. When code was generated quickly, instability usually comes from structure, missing error handling, and exposed secrets rather than from one bad function, so stabilisation comes first on the vibe-coded mobile app rescue track, and performance work follows once the app behaves predictably.

How we reproduce a crash we cannot see

A crash that only happens on certain devices is the ordinary case, and the cause rarely sits in the model number. It sits in what the model, the OS version, the locale, or the memory pressure implies: a smaller heap limit, an older graphics driver, a missing sensor, a keyboard in another language, or the app being killed in the background while a network call is in flight.

We start from the symbolicated stack trace, which needs uploaded debug symbols on iOS and a mapping file on Android. Without them a report is a list of hexadecimal addresses with no meaning, so restoring symbol upload is often the first task. Then we read the distribution: app version, OS versions, device models, the share of sessions affected, and the events leading up to the crash.

From there we rebuild the failing configuration on purpose: a release build with the same optimisation settings, because a debug build hides timing and memory behaviour, on a physical device with the matching OS version or on a cloud device farm when the exact model is hard to buy. We add breadcrumbs and structured logging, then drive the app toward the failing path repeatedly. Install it fresh, deny the permission, rotate the screen mid-request, fill storage until the system reclaims memory, move the device between networks.

Where the reproduction tightens but never becomes deterministic, we treat it as a race or a resource limit and fix it defensively: guard the lifecycle callback, cancel the abandoned task, handle the response that arrives after the screen is gone. Then the release ships as a staged rollout, and we confirm the fix against the crash-free session rate for the affected device group rather than one lab machine.

What we deliver

A ranked triage list in writing, ordered by how many users and how much revenue each failure touches, and the fixes merged and released through your store accounts. Symbol and build repairs where reporting was blind. A performance baseline and a matching after-measurement for cold start, warm start, frame timing, memory, and crash-free sessions. Instrumentation wired into analytics and crash reporting so the next regression is visible without us. A short release checklist covering staged rollout, rollback, and versioning.

How the engagement runs

The first days are triage: we read the reports, run the app, and separate the failures we can fix quickly from the ones that need investigation. You get that list before any large work is quoted, and we recommend fixing the top few by user impact.

Work then runs in short cycles, each ending with a build you can install and a note on what changed. Fixes go out behind staged rollouts, and crash and performance numbers come back within a day or two of a release. Where the app also needs dependency and platform upkeep, that continues under a mobile app maintenance arrangement. When a failure turns out to live in the backend rather than the client, it moves into API development and integration.

What it costs

Bug fixing and performance work runs at our flat rate of $39 per hour, because scope is set by what the failures turn out to be: a triage pass that finds ten small issues is a short order, while a crash caused by a race inside a third party SDK can consume days before a fix exists. No honest estimate exists before the reproduction is in hand.

We describe scope in weeks or phases rather than fixed figures. One phase is usually one to two weeks and covers a defined group of failures, such as startup, the camera flow, or the payment screens. Where the stable version of a feature has to be rebuilt rather than patched, that is a new-build order, and a mobile MVP of that shape sits in the typical range of $4,800 to $8,500 across four to six weeks on the pricing page. The same page lists monthly capacity plans from $2,699 for teams that want continuous attention.

Proof

Voice VPN is our in-house Flutter VPN with native Android and iOS network layers, where healthy connections settle in around one to two seconds. It proves we can chase timing and connection behaviour in an app whose hardest problem is the network itself.

CalculatorX, published as HideX, is a native Swift privacy utility that hides photos, videos, and contacts behind a calculator-style PIN screen. It proves we work inside shipped Swift code where a subtle defect can lock a user out of their own data.

AccuLevels is a React Native level and angle tool that reads the accelerometer and gyroscope, with calibration that corrects for device-specific sensor variance. It proves we handle behaviour that differs by hardware, the same problem behind most device-specific crashes.

Related services

• Mobile apps development for new iOS, Android, and Flutter builds rather than repairs
• App analytics and crash reporting to make failures visible before users report them
• Vibe-coded mobile app rescue for AI-built prototypes that need structure before they can be tuned
• Mobile app maintenance and support for ongoing fixes, dependency upkeep, and platform releases
• API development and integration for the server side of slow responses, timeouts, and sync faults

Next step

Send us the store link, the crash reports you have, and a sentence on what users complain about. We will tell you which failures are quick, which need investigation, and what the first phase would cover.

Contact us with the app details, or run the scope through the Vasilkoff.info estimator to get a starting range before you commit to anything.