Software quality assurance (SQA) helps you reduce incident risk, protect user trust, and keep engineering load predictable. We start with a quick audit, align on a risk based strategy, execute with your team and tooling, then report back with clear priorities and ownership so decisions stay in your hands. Trusted delivery matters, below you will see teams and products we have supported, so you can judge fit fast.
Why partner with us?
When a release goes wrong, the cost shows up fast in incidents, firefighting, and missed roadmap windows. Our quality assurance approach focuses on release readiness and early signal, so your team spends less time reacting and more time delivering. We translate quality objectives into a practical testing plan, set measurable quality metrics, and keep delivery predictable with a steady reporting cadence and clear ownership. If you also need broader delivery support beyond QA, scale your development while keeping one coherent workflow across teams. This is quality management that works with your existing workflow, not alongside it.
No risk. Just results. Get a feel for our process, speed, and quality — work with our developers for a trial sprint and see why global companies choose Selleo.
AI can assist your QA team by reducing repetitive work and improving triage accuracy, without replacing engineering judgment. We keep governance explicit with defined data boundaries, measurable KPIs, and a rollback plan, using quality data to monitor trends and keep decisions explainable. If you want to go deeper on implementation patterns, see our artificial intelligence solutions. The goal is faster feedback loops and a better signal to noise ratio when defects appear under real delivery pressure.
List of AI in QA use cases:
Do you need support with software quality? Take advantage of our consultations to connect with experts in cutting-edge technologies who can help streamline your project and minimize costs.
From quality assurance services to custom software and more. We provide the technology and expertise.
In enterprise environments, reliability and audit readiness matter as much as features. We align integration needs and quality requirements early, so teams ship with fewer surprises across complex software development constraints.
Talent platforms live or die on data accuracy, permissions, and trust in reporting. We design around domain constraints and workflow realities so your product stays consistent as teams, roles, and regions grow.
Self service HR requires simple UX over complex rules, plus dependable integrations into payroll and identity systems. We focus on predictability, privacy, and operational stability to keep support tickets down.
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Software quality assurance is the planned set of quality assurance activities that prevent defects across the entire lifecycle, from requirements to release. Quality control is the verification layer that checks results through software testing and QA testing, such as reviews and executions of test cases. In practice, software quality assurance connects quality management with day to day QA processes, so teams can ensure compliance with quality standards, industry standards, and defined standards. It also supports quality management systems by keeping evidence, audits, and related documents consistent and easy to trace.
Start by mapping business goals to measurable quality objectives, then translate them into quality requirements your software developers can build against. This is quality planning that aligns project management and the development process with clear acceptance criteria, risk management priorities, and release timing. Define what the final product must achieve in reliability, security, and usability, then decide how you will detect defects early using testing processes, quality metrics, and feedback loops. This keeps software quality visible and actionable instead of subjective.
Reliable QA processes rely on defined processes, clear ownership, and tight feedback loops between the QA team and engineers. The key elements usually include shared quality processes, consistent test case design, regression testing scope rules, and effective communication so issues do not get lost between roles. Add change management so updates to source code, environments, and configuration management do not break repeatability. Strong software engineering quality assurance practices also include lightweight documentation and predictable reporting so project managers can make decisions quickly.
Test automation works best when it protects critical paths on every change and stays portable in your stack. Use automation scripts that are readable, versioned with the source code, and paired with documented test cases so ownership stays with your team. Connect checks to build automation and configuration management to reduce environment drift and keep releases consistent. Treat automation as a QA toolkit, not a separate system, so software testing supports delivery speed while improving software quality and overall quality standards.
Regression testing verifies that new changes did not break existing behavior, especially around critical user flows and integrations. Prioritize using risk management and defect data, focusing on areas with frequent changes, high impact incidents, or recurring quality issues. Start with a small set of high value test cases, then expand coverage based on collected data, production signals, and quality metrics that monitor trends over time. This keeps testing processes efficient and prevents a bloated suite that slows releases.
Run performance testing when your product has traffic spikes, strict SLAs, complex queries, or new architecture changes that can affect latency. Focus on quality metrics that reflect real usage: response time, error rates, throughput, and resource saturation, then track results as collected data to monitor trends across releases. Tie outcomes to release gates so decisions are repeatable and aligned with overall quality standards. Done well, performance testing supports high quality software and reduces incident risk without slowing software development.
Unit testing validates small components early, helping software developers catch mistakes before they propagate through the system. Static analysis complements this by scanning source code for common defects, security issues, and maintainability risks that are easy to miss in review. In this context, software refers to more than code: it includes configuration, dependencies, and integration points that can affect behavior. Combined, these testing types improve software engineering quality, reduce defect leakage, and keep the final product more stable.
Effective triage starts with consistent defect data and quality data: steps to reproduce, evidence, impact, and suspected scope. A quality engineer or QA engineer can group issues by risk, map them to owners, and keep a reporting cadence that project managers can act on. Use quality metrics that track reopen rates, escape rates, and time to resolution, then align priorities with product management goals. This approach keeps quality assurance transparent, reduces chaos, and helps teams detect defects earlier with clear next steps.
Software process improvement is a continuous loop of evaluating processes, identifying bottlenecks, and updating defined processes so teams continuously improve processes without adding overhead. Use effects analysis to understand why defects happen, then adjust quality assurance practices, tooling, and handoffs. Capture decisions in lightweight related documents so learning is not lost, and use change management to roll improvements out safely. This is practical quality improvement that strengthens quality management systems and protects delivery predictability.
An external team can add hands on experience, specialized tools, and a scalable QA team that supports your roadmap without overloading key contributors. You gain structured software quality assurance sqa, repeatable QA processes, and clearer quality control, while your engineers stay focused on building. The result is more consistent software testing, improved release readiness, and higher customer satisfaction as issues are found earlier. With the right collaboration, you also get documentation and portable assets that keep ownership in house and support long term quality management.
When developing software, time and cost are two crucial factors when choosing a software vendor. To speed up this process and reduce cost some companies offer Quality Assurance services. Is it really needed or worth it? Let’s find out!
To perfectly test an app from each side, you need a great testing tool. This time we analyze specific examples and explain why Maestro was chosen by our QA engineers!
Cloud infrastructure is essential for modern businesses, helping them improve agility, scalability, and innovation. At its core, cloud infrastructure includes the hardware and software that support cloud computing, such as servers, storage, networking, and virtualization technologies.
