- Melbourne · New Delhi
- 10 years (since 2016)
- AEST overlap (4–6 business hours)
- AWS Sydney (ap-southeast-2)
Testing We Perform
Testing We PerformImagine launching a new app that gets bombarded with crash complaints within hours of launch. Don't worry; we will never let that happen to your app. Founded in 2016, our team brings over two decades of combined experience in software testing, so we know about finding and fixing those pesky glitches. Think of us as your software's best friend, giving it a thorough checkup before it goes into the world.
Doing random clicks is not our way of working. We dive deep into your code to find weak spots. Whether a web application or a complex enterprise system, we have the expertise to put it through rigorous tests. From functional testing to performance tests and security audits, we ensure your software is ready to impress your users. Happy users mean a growing business.
Our comprehensive testing approach covers every aspect of your software, from initial development stages through post-launch maintenance. We work closely with your development team to integrate quality assurance seamlessly into your workflow, ensuring that quality is built in from the start rather than tested in at the end. Beyond functional correctness, our non-functional testing practice covers performance, security, accessibility, and compatibility — the quality attributes that determine whether software survives real-world conditions.
Applications We Test
Our testing services cover web applications, mobile apps, desktop software, embedded systems, enterprise solutions, APIs, and games. Each application type requires specialised testing approaches, and our experienced team has the expertise to ensure your software works flawlessly regardless of platform.
Web Applications
Their testing is focused on checking functionality, usability, security, performance, and compatibility across different browsers.
Mobile Applications
They are tested for core functionality, usability, performance, and compatibility across different devices and network conditions.
Desktop Applications
We Test them for functionality, usability, performance, and compatibility.
Embedded Systems
Small software codes are embedded within hardware devices to perform a specific function. Testing that code is crucial to ensure user safety.
Enterprise Software
We have a niche skill set and extensive experience testing these large applications.
API Testing
We test intercommunication between different systems through APIs. Our focus is to test the functionality, performance, and security.
Game Testing
We do play games, but not just for fun. Instead, we play to review them for bugs, which requires specialised techniques and setup to cover different aspects.
Apps We Test
At Brainstack, we're not just experts in software testing; we're mobile app aficionados. We have extensive experience testing a diverse range of mobile applications.
Services We Provide
Real engineering practice — specific tools, applied to the failure modes that matter, calibrated for the work patterns we actually ship.
01Functional Testing
Functional testing verifies that the software does what its specification says — every user story, every API endpoint, every form submission. Our approach is specification-driven: each test case ties back to a written requirement so coverage is measurable rather than vibes-based.
We use TestRail or Xray for Jira (the latter widespread across Australian enterprises on the Atlassian stack) for test case management, and combine manual exploratory testing with automated suites in Playwright (TypeScript) and Cypress for web flows, plus Postman + Newman for API contracts. Suites run on every PR via GitHub Actions, GitLab CI, or Bitbucket Pipelines depending on the customer's CI choice.
For domain-heavy work — EUDR commodity traceability, sustainability certification platforms — we maintain a separate domain-rules layer of tests that codifies the regulatory requirements as executable specifications. Audit trails fall out naturally.
Read more about Functional Testing
02Automation Testing
Automation testing pays back only when the suite is fast, reliable, and reflective of real user paths. We design suites around the test pyramid — heavy on unit and API tests, lean on UI, parallelised so the full regression run completes in under 30 minutes.
Our stack: Playwright (TypeScript) for new web projects given its parallel-by-default architecture and skill-shortage talent profile, Cypress where developer experience preference dictates, and Selenium WebDriver where existing investments or specific browser-grid requirements call for it. For mobile: Appium for cross-platform, XCUITest (iOS) and Espresso (Android) where native depth matters — both flagged as skill-shortage roles in the AU market. For API: Postman + Newman in CI, REST Assured for Java services, Karate DSL for BDD-style flows.
We wire suites into GitHub Actions, Jenkins, or Bitbucket Pipelines (a common pick in Australian enterprises with Atlassian-stack tooling) and report through Allure or ReportPortal so failure trends are visible across releases, not just per-run.
Read more about Automation Testing03Regression Testing
Regression testing protects what already works. The failure mode is suite bloat: tests grow until they're too slow to run, teams skip them, and production starts catching what tests should have. Our defence is risk-based selection — we tag tests by feature area and risk level, run the high-risk subset on every PR (~10 minutes), and the full suite nightly.
Tooling: Playwright with sharded parallel runs for UI; framework-native resilience patterns first (Playwright's auto-waiting and locator strategies, Cypress's retry-ability) to minimise locator drift; Applitools Eyes for visual regression on dashboard-heavy products.
For long-running engagements — multi-region rollouts, compliance platforms like EUDR traceability — we instrument test failures through OpenTelemetry so flaky-test patterns surface early. Fixing flakiness is a first-class engineering problem, not a chore relegated to whoever runs CI that week.
Read more about Regression Testing
04AI-Enabled Testing
The honest version: AI helps testing in narrow, specific ways today — and pretending otherwise damages credibility more than it helps. We use AI testing tools where they genuinely save time, and we don't where they don't.
Where AI delivers measurable ROI in our work:
- Self-healing locators (commercial vendors) — Testim, Mabl, and Functionize report 60–80% UI test maintenance reductions on highly volatile front-ends (Tricentis customer case studies, 2024). We recommend evaluating these for your context; our default is framework-native resilience first and commercial self-healing layered on top only where the maintenance burden justifies it. Best fit: dashboard products, admin consoles.
- Visual regression with AI diff (Applitools Eyes, Percy) — catches CSS regressions that pixel-diff tools miss. Best fit: marketing sites, design-system migrations.
- Test case generation from tickets — Claude or ChatGPT translating PRDs into Gherkin acceptance scenarios, reviewed by a tester before commit. Time saver, not a substitute for analysis.
- Code-assisted unit test scaffolding — GitHub Copilot or Claude for scaffolding new tests from function stubs and existing patterns, always reviewed and never blindly merged. Legacy code coverage gaps we handle through manual audit and targeted unit-test writing, not AI generation.
Where we don't use AI yet: critical security testing (humans + Burp Suite still win), fintech transaction validation (deterministic over probabilistic), regulatory paths where false confidence is worse than false positives. We recommend against in writing when a customer asks us to.
Read more about AI-Enabled Testing05Integration Testing
Integration testing verifies that components work correctly together — not in isolation. The failure modes that matter most are the ones that only appear when services cross boundaries: contract drift between a CRM and a geolocation pipeline (the EUDR pattern), or sync queues that work in dev but stall under realistic field conditions (the offline-first PWA pattern from our agritech work). Static unit tests don't catch either.
Our integration suites combine three layers: contract testing with Pact for consumer-driven verification, REST Assured (Java) and Karate DSL for service-to-service flows, and Postman + Newman for end-to-end API regression. We wire these into GitHub Actions, Jenkins, or Bitbucket Pipelines depending on the customer's CI choice. The goal is to catch schema drift at PR time, not in production.
On EUDR-style supply chain platforms, this stack catches a common and high-impact regression — provider-side schema changes that silently break importers. We recommend pairing contract tests with traffic shadowing on staging environments (blue-green deployments, canary releases) to validate against realistic load before cutover.
Read more about Integration Testing
06Performance Testing
Performance testing answers three different questions: how does the system behave under expected load (baseline), under sustained heavy load (soak), and under sudden spikes (burst). Each load shape needs its own test profile and its own SLO. Treating them as one job is the most common failure mode we inherit from previous teams.
Our load-generation stack: k6 (modern JavaScript-based, skill-shortage tool) for new projects, JMeter for legacy and customers with existing test investments, Gatling (Scala) for high-throughput scenarios where k6's per-VU overhead becomes the bottleneck. We run load generators from AWS Sydney (ap-southeast-2) for Australian customers so latency measurements reflect what end users actually see.
Observability is the half of performance engineering people skip. We instrument every load test with Grafana + Prometheus or Datadog, capture trace samples with OpenTelemetry, and track results against published SLOs over time — not just per-release. Real regressions usually show up as slow drift across 4–6 sprints, not as one-off cliffs.
Read more about Performance Testing07Mobile Testing
Mobile testing has two dimensions most non-mobile QA teams underestimate: device fragmentation (Android especially), and field conditions (low bandwidth, intermittent GPS, low storage, drained battery). Both bite production hard if test coverage skips them.
We test against real devices via BrowserStack and Sauce Labs (cloud labs covering current and N-2 OS versions for iOS and Android), including their physical-device fleets for edge-case behaviour like flight-mode transitions, low-battery scenarios, and degraded-network conditions. For automation: Appium for cross-platform regression, plus XCUITest (iOS) and Espresso (Android) where native depth matters — both flagged as skill-shortage roles in Australian recruitment data.
Our offline-first PWA work for a US-based sustainability leader — sync queue conflict resolution, IndexedDB storage limits across browsers, GPS-flake handling — is a representative example of where field-condition test coverage matters most. The bug that ships isn't the one caught in the dev lab. It's the one that surfaces at sub-3G coverage in a rural commodity-buying station.
Read more about Mobile Testing
08Security Testing
Security testing on web and API surfaces starts with the OWASP Top 10 and OWASP API Top 10 as a baseline, then layers domain-specific threat modelling on top. Generic checklists catch generic bugs; the painful breaches usually live in business logic.
Our toolchain: OWASP ZAP for automated baseline scanning in CI (DAST), Burp Suite Professional for manual deep-dive — still the industry standard for offensive security work and a long-term skill-shortage role. Snyk and Dependabot for dependency tracking, Trivy for container image scans, Semgrep and SonarQube for static analysis (SAST), Nuclei for template-based vulnerability scanning across exposed surface area.
For fintech-style customers we additionally run authenticated scans, IDOR-class manual testing, and JWT/OAuth flow review. For EUDR-style compliance platforms we test data-residency assumptions (does Australian or EU PII actually stay where the privacy notice claims it does) and audit-trail tamper-resistance. These are the things automated scanners don't find.
Read more about Security Testing09QA Consultancy
QA consultancy work, in our experience, is rarely about adding tests. It's about diagnosing why an existing testing investment isn't paying back: brittle suites that can't be trusted, no risk-based selection so suites are too slow to run, no observability so flakiness goes unaddressed, no contract layer so integration breaks ship to production.
Every engagement starts with a two-week diagnostic: review of the existing test suite's pass/fail history, analysis of CI run times, audit of the test pyramid shape, and interviews with developers about which tests they actually trust. The output is a written report with a 30-60-90-day plan — not a deck.
Common patterns we end up implementing: introducing contract testing (Pact) where there was none, migrating UI tests from legacy frameworks to Playwright, setting up Allure or ReportPortal for trend analysis, instrumenting test results into the same observability stack as production (Grafana, Datadog). Where customers use Jira we can wire Xray or Zephyr so product owners see the same quality view the QA team sees.
Read more about QA Consultancy
10QA On Demand
QA on-demand fills three specific gaps we see repeatedly:
- Release-cycle surge — your team has a launch, you need 2–3 testers for 4–8 weeks, then back to zero. Permanent hires are wasteful; contractor onboarding is overhead. We embed a small surge team that integrates into your existing rituals (standups, sprint planning, your test management tool).
- Specialised skill, short window— performance with k6 or JMeter, security with Burp Suite, mobile native with XCUITest or Espresso, AI-assisted automation with Testim or Mabl. These are skill-shortage areas in the Australian and broader APAC market; pulling them in for the project they're needed on is more economical than a full-time hire.
- Independent verification before a critical release — fresh eyes on a high-risk launch. Regulatory compliance audits, board demos, M&A technical due diligence.
We work in your tools (Jira, Xray, Zephyr, TestRail), your CI (GitHub Actions, GitLab, Bitbucket Pipelines, Jenkins), and your delivery model (Scrum, Kanban, SAFe). No methodology imposition. No starter-pack templates we insist on using.
Read more about QA On DemandSoftware Testing Workflow
Our software testing workflow is meticulous, leaving no room for error. We leverage proven methodologies and advanced tools to ensure your product is robust, reliable, and ready to impress.
Initiation Phase
The Initiation Phase starts by examining current practices, reviewing documents, and evaluating the current state to identify strategy weaknesses and reasons for poor product quality.
Planning Phase
The planning phase includes identifying business risks, formulating mitigation strategies, planning tests, and setting KPIs. This phase requires meticulous planning as it directly affects results.
Implementation Phase
Implementation includes designing test environments, implementing test strategies, and evaluating performance. This is where execution starts delivering results, assessed using the set KPIs.
Support Phase
Support includes mentoring and training teams, forming backup plans, and identifying new problems. It also includes round-the-clock support.
Adapting to Change
Catch defects before your users do — systematically, not by luck.
Defects Caught Earlier, Fixed Cheaper
Every PR passes linting, unit, and integration checks before merge. This catches defects at the cheapest stage of the lifecycle and prevents late-cycle cleanup that burns sprint capacity.
Ready to Elevate Your Software Quality?
Tell us about your testing needs and get a free, no-obligation assessment from our QA team.
Domains We Serve
Our software delivery and AI work spans regulated, data-intensive industries where technology drives measurable outcomes.
Financial Services
Data analytics platforms, portfolio reporting dashboards, and automated compliance systems for asset managers. Real-time data pipelines, secure API integrations with banking middleware, and regulatory reporting modules tailored to regional requirements.
Healthcare
Cloud-based platforms for clinical workflow management, patient data systems, and telehealth integrations. HIPAA-aware architectures with compliance-first development where data privacy and audit trails are non-negotiable.
AgriTech & Sustainability
Offline-capable field data collection platforms and supply chain compliance tools deployed across East Africa, South America, and South Asia. PWAs with local data sync, SMS fallback, and voice interfaces. EUDR compliance workflows, traceability mapping, and certification body integration.
E-Commerce
Custom shopping experiences, inventory management systems, and order fulfilment automation. Headless commerce backends, payment gateway integrations, and real-time analytics to optimise conversion funnels.
Engagement Models
We tailor delivery to your team structure and ownership preference. For full process detail, review the dedicated engagement model page.
Outsourcing
- Outcome-based delivery ownership
- Managed roadmap, QA, and releases
- Best for end-to-end product builds
Staff Augmentation
- Engineers integrated into your team
- You keep sprint and release control
- Best for scaling delivery capacity fast
Tech Consulting
- Architecture and platform strategy guidance
- Roadmap, risk, and cost optimisation
- Best for audits, modernisation, and decision support
Useful Reads
Stay updated with the latest insights, trends, and best practices in software industry through our related blog posts.
Frequently Asked Questions
Common questions about our software testing and quality assurance services, testing methodologies, and how we help ensure your applications meet the highest quality standards.