Software Reliability Through DevOps and SRE – How Gart Can Help You Build a Reliable Digital Solution

Site Reliability Engineering (SRE) focuses on keeping services reliable and scalable. A crucial part of this discipline is monitoring, which is where the concept of Golden Signals comes into play.  By focusing on just four “Golden Signals,” organizations can cut their incident response time in half. Golden Signals help teams quickly identify and diagnose issues within a system. This post explores how SRE teams use these metrics — latency, errors, traffic, saturation—to drive reliability and streamline troubleshooting in complex microservices environments. What are the four golden signals in SRE SRE principles streamline monitoring by focusing on four key metrics—latency, errors, traffic, and saturation—collectively known as Golden Signals. Instead of tracking numerous metrics across different technologies, focusing on these four metrics helps in quickly identifying and resolving issues. Latency: Latency is the time it takes for a request to travel from the client to the server and back. High latency can cause a poor user experience, making it critical to keep this metric in check. For example, in web applications, latency might typically range from 200 to 400 milliseconds. Latency under 300 ms ensures good user experience; errors >1% necessitate investigation. Latency monitoring helps detect slowdowns early, allowing for quick corrective action. Errors:Errors refer to the rate of failed requests. Monitoring errors is essential because not all errors have the same impact. For instance, a 500 error (server error) is more severe than a 400 error (client error) because the former often requires immediate intervention. Identifying error spikes can alert teams to underlying issues before they escalate into major problems. Traffic:Traffic measures the volume of requests coming into the system. Understanding traffic patterns helps teams prepare for expected loads and identify anomalies that might indicate issues such as DDoS attacks or unplanned spikes in user activity. For example, if your system is built to handle 1,000 requests per second and suddenly receives 10,000, this surge might overwhelm your infrastructure if not properly managed. Saturation:Saturation is about resource utilization; it shows how close your system is to reaching its full capacity. Monitoring saturation helps avoid performance bottlenecks caused by overuse of resources like CPU, memory, or network bandwidth. Think of it like a car's tachometer: once it redlines, you're pushing the engine too hard, risking a breakdown. Challenges associated with monitoring saturation in microservices: Complexity of Microservice Architectures:In microservice environments, various services are often built on different technologies (e.g., Node.js, databases, Swift). Each service may handle resource usage differently, making it challenging to monitor and understand overall system saturation accurately. Saturation occurs when resources such as CPU, memory, or network bandwidth are fully utilized, leading to degraded performance. Resource Utilization Visibility:Since each microservice can have its unique metrics, gaining a clear view of overall saturation is difficult. Teams need to aggregate and standardize data from multiple services to accurately assess saturation levels. This can be time-consuming and requires expertise across different technology stacks. Identification of Bottlenecks:Saturation often results in bottlenecks where some services are overloaded while others are underutilized. Pinpointing which service is causing the bottleneck in a complex system can be difficult without a cohesive monitoring approach like the one provided by SRE Golden Signals. Dynamic and Variable Loads:In microservice architectures, traffic and resource demands can fluctuate rapidly, making it essential to monitor saturation in real-time. Services must adapt to changes in load, but without proper monitoring, it's easy to miss critical saturation points that can impact overall system performance. Why Golden Signals Matter Golden Signals provide a comprehensive overview of a system's health, enabling SREs and DevOps teams to be proactive rather than reactive. By continuously monitoring these metrics, teams can spot trends and anomalies, address potential issues before they affect end-users, and maintain a high level of service reliability. SRE Golden Signals help in proactive system monitoring SRE Golden Signals are crucial for proactive system monitoring because they simplify the identification of root causes in complex applications. Instead of getting overwhelmed by numerous metrics from various technologies, SRE Golden Signals focus on four key indicators: latency, errors, traffic, and saturation. By continuously monitoring these signals, teams can detect anomalies early and address potential issues before they affect the end-user. For instance, if there is an increase in latency or a spike in error rates, it signals that something is wrong, prompting immediate investigation.  What are the key benefits of using "golden signals" in a microservices environment? The "golden signals" approach is especially beneficial in a microservices environment because it provides a simplified yet powerful framework to monitor essential metrics across complex service architectures. Here’s why this approach is effective: ▪️Focuses on Key Performance Indicators (KPIs) By concentrating on latency, errors, traffic, and saturation, the golden signals let teams avoid the overwhelming and often unmanageable task of tracking every metric across diverse microservices. This strategic focus means that only the most crucial metrics impacting user experience are monitored. ▪️Enhances Cross-Technology Clarity In a microservices ecosystem where services might be built on different technologies (e.g., Node.js, DB2, Swift), using universal metrics minimizes the need for specific expertise. Teams can identify issues without having to fully understand the intricacies of every service’s technology stack. ▪️Speeds Up Troubleshooting Golden signals quickly highlight root causes by filtering out non-essential metrics, allowing the team to narrow down potential problem areas in a large web of interdependent services. This is crucial for maintaining service uptime and a seamless user experience. By applying these golden signals, SRE teams can efficiently diagnose and address issues, keeping complex applications stable and responsive. How to Monitor Microservices Using Golden Signals Monitoring microservices requires a streamlined approach, especially in environments where dozens (or hundreds) of services interact across various technology stacks. Golden Signals provide a clear, focused framework for tracking system health across these distributed systems. 1. Start by Defining What You’ll Monitor Each microservice should have its own observability pipeline for: Latency – Measure the time it takes for a request to be processed from start to finish. Errors – Capture both 4xx and 5xx HTTP codes or application-level exceptions. Traffic – Monitor request rates (RPS/QPS) and message throughput. Saturation – Track CPU, memory, thread usage, and queue lengths. Tip: Integrate these signals into SLIs (Service Level Indicators) and SLOs (Service Level Objectives) to measure system reliability over time. 2. Use Unified Observability Tools Deploy tools that allow you to collect metrics, logs, and traces across all services. Popular platforms include: Datadog and New Relic: Full-stack observability with built-in Golden Signals support. Prometheus + Grafana: Open-source, highly customizable metrics + dashboards. OpenTelemetry: Instrument code once to collect traces, metrics, and logs. 3. Isolate Service Boundaries Microservices should expose telemetry endpoints (e.g., /metrics for Prometheus or OpenTelemetry exporters). Group Golden Signals by service for clarity: MicroserviceLatencyError RateTrafficSaturationAuth220ms1.2%5k RPS78% CPUPayments310ms3.1%3k RPS89% Memory 4. Correlate Signals with Tracing Use distributed tracing to map requests across services. Tools like Jaeger or Zipkin help you: Trace latency across hops Find the exact service causing spikes in error rates Visualize traffic flows and bottlenecks 5. Automate Alerting with Context Set thresholds and anomaly detection for each signal: Latency > 500ms? Alert DevOps Saturation > 90%? Trigger autoscaling Error Rate > 2% over 5 mins? Notify engineering and create an incident ticket How can the "one-hop dependency view" assist in troubleshooting? The "one-hop dependency view" in application performance monitoring (APM) simplifies troubleshooting by focusing only on the services that directly impact the affected service. Here’s how it helps: ▪️Reduces Investigation Scope Rather than analyzing the entire microservices topology, the one-hop view narrows the scope to immediate dependencies. This selective approach allows engineers to focus on the most likely sources of issues, saving time in identifying the root cause. ▪️Streamlines Root-Cause Analysis By examining only the services one level away, the team can apply the golden signals (latency, errors, traffic, saturation) to detect any anomalies quickly. If a direct dependency is experiencing problems, it becomes immediately apparent without unnecessary complexity. ▪️Decreases Mean-Time-to-Recovery (MTTR) With fewer services to investigate, the MTTR is significantly reduced. Engineers can identify and address the root issue faster, minimizing downtime and maintaining the application’s reliability. Using the one-hop dependency view helps SRE teams keep the troubleshooting process efficient, especially in complex, interdependent service ecosystems Practical Application: Using APM Dashboards Application Performance Management (APM) dashboards integrate Golden Signals into a single view, allowing teams to monitor all critical metrics at once. For example, the operations team can use APM dashboards to get insights into latency, errors, traffic, and saturation. This holistic view simplifies troubleshooting and reduces the mean time to resolution (MTTR). Here's how they work together: ▪️Centralized Monitoring with APM Dashboards:APM tools provide dashboards that centralize the key Golden Signals—latency, errors, traffic, and saturation. This centralized view allows operations and development teams to monitor the health of their applications in real-time. By displaying these critical metrics in one place, APM tools simplify the identification of performance issues, making it easier to spot trends and anomalies that need attention. ▪️"One Hop" Dependency Views:APM tools often support a "one hop" dependency view, which shows only the immediate downstream services connected to a problematic service. This feature is particularly useful in complex microservice environments where pinpointing the root cause of an issue can be daunting. By focusing on immediate dependencies, teams can quickly assess which services are functioning within normal parameters and which are experiencing issues, thereby speeding up the troubleshooting process. ▪️Proactive Issue Detection and Resolution:Integrating Golden Signals into APM tools allows for proactive monitoring, where issues can be identified before they escalate into more serious problems. For example, if a service’s saturation levels begin trending upwards, the APM tool can alert the team before users experience degraded performance. This proactive approach helps reduce the mean time to resolution (MTTR) and improves overall service reliability. ▪️ Customization for Different Teams:The video also mentions that APM tools can be customized for different stakeholders within the organization. While the operations team may focus on all four Golden Signals, development teams might create specialized dashboards that prioritize the signals most relevant to their services. This tailored approach ensures that both dev and ops teams are aligned and can address issues quickly, often even before they impact the end-users. In essence, the integration of SRE Golden Signals with APM tools empowers teams to maintain high levels of service performance and reliability by providing clear, actionable insights into the most critical aspects of their systems. What is the significance of distinguishing 500 vs. 400 errors in SRE monitoring? The distinction between 500 and 400 errors in SRE monitoring is crucial because it impacts how issues are prioritized and addressed. Here’s a breakdown: Error TypeCauseSeverityResponse500 Server-side issueSystem/app failureHighImmediate investigation400 Client-side request issueBad input/authLowerMonitor trends only 500 Errors (Server Errors) These indicate serious problems on the server side, such as downtime or crashes. They require immediate attention because they prevent users from accessing the service entirely, often resulting in significant disruptions. For instance, a 500 error signals that something is failing within the server's infrastructure, meaning end-users can’t receive a response at all. Therefore, these errors are more critical in incident response and may trigger alerts for the SRE team. 400 Errors (Client Errors) These typically indicate client-side issues, where a request is invalid or needs adjustment, like when the requested resource doesn’t exist or is restricted. Such errors might be resolved simply by retrying or by the client correcting the request, so they’re usually less urgent. Monitoring 400 errors can still reveal trends or user behavior that may require attention, but they don't indicate systemic issues. In summary, recognizing the difference allows SREs to prioritize resources on issues that directly affect the system’s reliability and availability (like 500 errors) versus issues that may just need minor adjustments or retries. SRE Monitoring Dashboard Best Practices A well-structured SRE dashboard makes or breaks your incident response. It’s not just about displaying data — it’s about surfacing the right insights at the right time. Here's how to do it: 1. Prioritize Golden Signals Above All Place latency, errors, traffic, and saturation front and center. Avoid clutter—these four are your frontline defense against performance issues. Example Layout: Top row: Latency (P50/P95), Error Rate (%), Traffic (RPS), Saturation (CPU, Memory) Second row: SLIs, SLO burn rates, alerts over time 2. Use Visual Cues Effectively Color code thresholds: green (healthy), yellow (warning), red (critical) Sparklines for trend visualization Heatmaps to spot saturation across clusters or zones 3. Break Down by Environment & Service Segment dashboards by: Environment (prod, staging, dev) Service or team ownership Availability zone or region This helps you quickly isolate issues when incidents arise. 4. Integrate Logs and Traces Link metrics to logs or traces: Click on a spike in latency → see related trace in Jaeger or logs in Kibana Integrate dashboards with alert management (PagerDuty, Opsgenie) 5. Provide Different Views for Different Teams SRE/DevOps view: Full stack overview + real-time alerts Engineering view: Deep dive into a specific service’s metrics Management view: SLO dashboards and service health summaries Use templating (in Grafana or Datadog) so one dashboard serves multiple roles. 6. Regularly Review & Evolve Dashboards Prune unused panels or metrics Reassess thresholds quarterly Add annotations for incidents or deployments Dashboards should be living documents, not static reports. Learn from the official Google documentation. Conclusion Ready to take your system's reliability and performance to the next level? Gart Solutions offers top-tier SRE Monitoring services to ensure your systems are always running smoothly and efficiently. Our experts can help you identify and address potential issues before they impact your business, ensuring minimal downtime and optimal performance. Discover how Gart Solutions can enhance your system's reliability today! Learn from our IT Monitoring case studies (Monitoring Solution for a B2C SaaS Music Platform and Advanced Monitoring for Digital Landfill Management) to learn more about our SRE Monitoring expertise. After implementing Golden Signals, our customer reduced MTTR by 60% in under two months. https://youtu.be/BqPXUxhshTM?si=EWFFu0JNYgJCj7g0

Is your software ready for growth, or will it crumble under pressure? Businesses are under immense pressure to innovate and grow. While technology is the backbone of these advancements, understanding its intricacies can be a daunting task for non-technical business owners. This is especially true when it comes to complex concepts like scalability. Scalability is the ability of a system to handle increasing workloads and user demands. Without it, businesses risk experiencing slow performance, system crashes, and ultimately, lost customers. It's the difference between a website that can handle a sudden surge in traffic during a holiday sale and one that crashes under the pressure. This is where the disciplines of DevOps and Site Reliability Engineering (SRE) come into play. These complementary practices, which have gained significant traction in the tech industry, offer a roadmap for ensuring the scalability and resilience of your digital projects without sacrificing reliability. This guide dives into how scaling delivers business ROI, the practices that make it possible, and the strategic partnership Gart Solutions provides. Understanding Scalability Pilots are easy, but scaling up is hard Scalability is simply the ability of a system to grow and handle increased demand. Imagine a small restaurant that becomes incredibly popular. If it can't expand its kitchen or seating, it will struggle to serve more customers. A scalable restaurant, on the other hand, can adjust its operations to accommodate the growing crowd. The consequences of poor scalability can be dire for your business. Imagine your company's website grinding to a halt during a major marketing campaign, frustrating potential customers and causing them to abandon their shopping carts or search for your competitors. Or consider the impact of a critical business application crashing under the strain of increased usage, leading to lost productivity, missed deadlines, and dissatisfied clients. The consequences of poor scalability extend beyond lost customers and revenue. A system that can't handle increased demand can damage a company's reputation. Major online retailers like Amazon or ticket sales platforms have invested heavily in scalability to prevent these issues during peak shopping periods. They understand that a seamless customer experience is crucial to their success. Scaling for Success: The Proven Path to Revenue Growth and Cost Savings Recent research from the Boston Consulting Group (BCG) has shed light on the tangible business benefits of scaling digital solutions. The study, which covered approximately 2,000 global companies, found that scaling individual digital solutions can generate revenue increases of 9% to 25% and cost savings of 8% to 28% compared to the relevant baseline (see Exhibits 2 and 3). But the real game-changer emerges when companies scale several digital solutions across the enterprise. In these cases, the research indicates that organizations can achieve an enterprise-wide revenue increase of almost 17%, along with a 17% reduction in costs. Individual digital solutions saw 9–25% revenue growth and 8–28% cost savings Enterprise-wide scaling resulted in ~17% revenue increase and ~17% cost reduction. The advantages of scaling digital solutions extend beyond just the financial bottom line. Businesses that successfully scale their digital capabilities also experience qualitative benefits, such as: Reimagined customer experiences that drive loyalty and satisfaction Greater ability to integrate digital and data ecosystems for competitive advantage Stronger business resilience and adaptability to market changes More inclusive and diverse workplaces that foster innovation How DevOps and SRE Practices Enable Scalability It's a valid question, and one that deserves a clear, practical explanation. Let's dive in and explore the key ways these complementary disciplines can future-proof your technology investments. Automation One of the core principles of DevOps is the automation of repetitive tasks, such as software deployment, infrastructure provisioning, and testing. By automating these processes, you can significantly reduce the time and effort required to scale your project. Imagine being able to spin up new servers or deploy the latest version of your application with just a few clicks – that's the power of DevOps automation. Infrastructure as Code (IaC) DevOps and SRE emphasize the use of IaC, where your infrastructure is defined and managed using code, rather than manual, error-prone processes. This approach makes it much easier to replicate and scale your infrastructure as your business grows. It's like having a digital blueprint that you can use to quickly and consistently build out new environments. Continuous Integration and Continuous Deployment (CI/CD) DevOps practices like CI/CD help to automate the entire build, test, and deployment pipeline. This means that changes to your codebase can be quickly and reliably rolled out to production, supporting faster iterations and scalability. Imagine being able to launch new features or updates without the risk of lengthy downtime or service disruptions. Monitoring and Observability SRE places a strong emphasis on monitoring and observability, which are essential for understanding the health and performance of your digital systems. By implementing robust monitoring tools and practices, you can quickly identify bottlenecks, performance issues, and other problems that may arise as you scale your project. This allows you to address challenges proactively, rather than waiting for your customers to experience the impact. Read more: Monitoring DevOps: Types, Practices, and Tools Scalable Architecture DevOps and SRE encourage the adoption of scalable architectural patterns, such as microservices, serverless, and cloud-native approaches. These modern architectural styles make it much easier to scale individual components of your project independently, rather than having to scale the entire system at once. It's like building with Lego blocks – you can add or remove pieces as needed without disrupting the whole structure. Read more: Cloud Scalability: Horizontal vs. Vertical Scaling of IT Infrastructures Capacity Planning SRE practices include proactive capacity planning, where you continuously monitor and forecast the resource requirements of your system. This allows you to scale your infrastructure and resources ahead of time, avoiding sudden spikes in demand that could cause performance issues or service disruptions. Incident Response and Resilience DevOps and SRE focus on building resilient systems that can withstand failures and recover quickly. This includes implementing practices like chaos engineering, incident response, and self-healing mechanisms. By making your digital solutions more robust and reliable, you can ensure that they continue to function smoothly even as you scale to meet growing demands. DevOps vs. SRE: Complementary Strengths for Scaling AspectDevOpsSREApproachCulture + automation toolsReliability engineering with metricsScalability EnablementCI/CD, IaCCapacity planning, error budgets, resiliencyGoalFast, consistent releasesReliable operation during growthFocusDevelopment process optimizationSystem availability and error management By adopting these DevOps and SRE practices, you can unlock the true scalability of your digital projects, empowering your business to adapt and thrive in the face of changing market conditions and customer needs. It's a strategic investment that will pay dividends for years to come. Key considerations for scalability: Vertical scaling: Increasing resources of existing hardware (e.g., CPU, RAM). Horizontal scaling: Adding more servers or instances to distribute the load. Load balancing: Distributing incoming traffic across multiple servers. Caching: Storing frequently accessed data for faster retrieval. Database optimization: Improving database performance to handle increased data volume. Cloud computing: Leveraging elastic resources for on-demand scalability. Understanding your business needs is the first step. What challenges are you facing? Are you looking to accelerate development, improve system reliability, or optimize costs? Having a clear picture of your requirements will help you find a partner that aligns with your objectives. The capacity to scale your digital solutions is no longer a nice-to-have – it's a strategic imperative. The companies that master this art will be well-positioned to outpace the competition, capitalize on growth opportunities, and future-proof their success. The choice is clear: you can continue to rely on outdated, manually intensive processes that put your business at risk of performance issues, service disruptions, and lost revenue, or you can invest in the proven practices that will transform your digital operations and position your company for sustainable growth. How Gart Solutions Drives Scalable Performance Gart combines consulting and hands-on delivery across: Automation services: IaC with Terraform, CI/CD pipelines Observability platforms: Prometheus, Grafana, CloudWatch setups Architecture design: Microservices, container orchestration (ECS/EKS) Capacity forecasting: Scaling planning, cloud resource optimization Incident readiness: Auto‑remediation, runbook development, SRE coaching Scale your business without limits. Contact Gart today.

You see, building software is a lot like cooking your favorite dish. Just as you add ingredients to make your meal perfect, software developers consider various elements to craft software that's top-notch. These elements, known as "software quality attributes" or "non-functional requirements (NFRs)," are like the secret spices that elevate your dish from good to gourmet.  Questions that Arise During Requirement Gathering When embarking on a software development journey, one of the crucial initial steps is requirement gathering. This phase sets the stage for the entire project and helps in shaping the ultimate success of the software. However, as you delve into this process, a multitude of questions arises 1. Is this a need or a requirement? Before diving into the technical aspects of a project, it's essential to distinguish between needs and requirements. A "need" represents a desire or a goal, while a "requirement" is a specific, documented statement that must be satisfied. This differentiation helps in setting priorities and understanding the core objectives of the project. 2. Is this a nice-to-have vs. must-have? In the world of software development, not all requirements are equal. Some are critical, often referred to as "must-have" requirements, while others are desirable but not essential, known as "nice-to-have" requirements. Understanding this distinction aids in resource allocation and project planning. 3. Is this the goal of the system or a contractual requirement? Requirements can stem from various sources, including the overarching goal of the system or contractual obligations. Distinguishing between these origins is vital to ensure that both the project's vision and contractual commitments are met. 4. Do we have to program in Java? Why? The choice of programming language is a fundamental decision in software development. Understanding why a specific language is chosen, such as Java, is essential for aligning the technology stack with the project's needs and constraints. Types of Requirements Now that we've addressed some common questions during requirement gathering, let's explore the different types of requirements that guide the development process: Functional Requirements Functional requirements specify how the system should function. They define the system's behavior in response to specific inputs, which lead to changes in its state and result in particular outputs. In essence, they answer the question: "What should the system do?" Non-Functional Requirements (Constraints) Non-functional requirements (NFRs) focus on the quality aspects of the system. They don't describe what the system does but rather how well it performs its intended functions.  Source: https://iso25000.com/index.php/en/iso-25000-standards/iso-25010 Functional requirements are like verbs  – The system should have a secure login NFRs are like attributes for these verbs  – The system should provide a highly secure login  Two products could have exactly the same functions, but their attributes can make them entirely different products. AspectNon-functional RequirementsFunctional RequirementsDefinitionDescribes the qualities, characteristics, and constraints of the system.Specifies the specific actions and tasks the system must perform.FocusConcerned with how well the system performs and behaves.Concentrated on the system's behavior and functionalities.ExamplesPerformance, reliability, security, usability, scalability, maintainability, etc.Input validation, data processing, user authentication, report generation, etc.ImportanceEnsures the system meets user expectations and provides a satisfactory experience.Ensures the system performs the required tasks accurately and efficiently.Evaluation CriteriaUsually measured through metrics and benchmarks.Assessed based on whether the system meets specific criteria and use cases.Dependency on FunctionalityIndependent of the system's core functionalities.Dependent on the system's functional behavior to achieve its intended purpose.Trade-offsBalancing different attributes to achieve optimal system performance.Balancing different functionalities to meet user and business requirements.CommunicationOften involves quantitative parameters and technical specifications.Often described using user stories, use cases, and functional descriptions. Understanding NFRs: Mandatory vs. Not Mandatory First, let's clarify that Functional Requirements are the mandatory aspects of a system. They're the must-haves, defining the core functionality. On the other hand, Non-Functional Requirements (NFRs) introduce nuances. They can be divided into two categories: Mandatory NFRs: These are non-negotiable requirements, such as response time for critical system operations. Failing to meet them renders the system unusable. Not Mandatory NFRs: These requirements, like response time for user interface interactions, are important but not showstoppers. Failing to meet them might mean the system is still usable, albeit with a suboptimal user experience. Interestingly, the importance of meeting NFRs often becomes more pronounced as a market matures. Once all products in a domain meet the functional requirements, users begin to scrutinize the non-functional aspects, making NFRs critical for a competitive edge. Expressing NFRs: a Unique Challenge While functional requirements are often expressed in use-case form, NFRs present a unique challenge. They typically don't exhibit externally visible functional behavior, making them difficult to express in the same manner. This is where the Quality Attribute Workshop (QAW) comes into play. The QAW is a structured approach used by development teams to elicit, refine, and prioritize NFRs. It involves collaborative sessions with stakeholders, architects, and developers to identify and define these crucial non-functional aspects. By using techniques such as scenarios, trade-off analysis, and quality attribute scenarios, the QAW helps in crafting clear and measurable NFRs. Good NFRs should be clear, concise, and measurable. It's not enough to list that a system should satisfy a set of NFRs; they must be quantifiable. Achieving this requires the involvement of both customers and developers. Balancing factors like ease of maintenance versus adaptability is crucial in crafting realistic performance requirements. There are a variety of techniques that can be used to ensure that QAs and NFRs are met. These include: Unit testing: Unit testing is a type of testing that tests individual units of code. Integration testing: Integration testing is a type of testing that tests how different units of code interact with each other. System testing: System testing is a type of testing that tests the entire system. User acceptance testing: User acceptance testing is a type of testing that is performed by users to ensure that the system meets their needs. The Impact of NFRs on Design and Code NFRs have a significant impact on high-level design and code development. Here's how: Special Consideration: NFRs demand special consideration during the software architecture and high-level design phase. They affect various high-level subsystems and might not map neatly to a specific subsystem. Inflexibility Post-Architecture: Once you move past the architecture phase, modifying NFRs becomes challenging. Making a system more secure or reliable after this point can be complex and costly. Real-World Examples of NFRs To put NFRs into perspective, let's look at some real-world examples: Performance: "80% of searches must return results in less than 2 seconds." Accuracy: "The system should predict costs within 90% of the actual cost." Portability: "No technology should hinder the system's transition to Linux." Reusability: "Database code should be reusable and exportable into a library." Maintainability: "Automated tests must exist for all components, with overnight tests completing in under 24 hours." Interoperability: "All configuration data should be stored in XML, with data stored in a SQL database. No database triggers. Programming in Java." Capacity: "The system must handle 20 million users while maintaining performance objectives." Manageability: "The system should support system administrators in troubleshooting problems." The relationship between Software Quality Attributes and NFRs As and NFRs are both important aspects of software development, and they are closely related.  Software Quality Attributes are characteristics of a software product that determine its quality. They are typically described in terms of how the product performs, such as its speed, reliability, and usability. NFRs are requirements that describe how the software should behave, but do not specify the specific features or functions of the software. They are typically described in terms of non-functional aspects of the software, such as its security, performance, and scalability. In other words, QAs are about the quality of the software, while NFRs are about the behavior of the software. The relationship between QAs and NFRs can be summarized as follows: QAs are often used to measure the fulfillment of NFRs. For example, a QA that measures the speed of the software can be used to measure the fulfillment of the NFR of performance. NFRs can sometimes be used to define QAs. For example, the NFR of security can be used to define a QA that tests the software for security vulnerabilities. QAs and NFRs can sometimes conflict with each other. For example, a software product that is highly secure might not be as user-friendly. It is important to strike a balance between  Software Quality Attributes and NFRs. The software should be of high quality, but it should also meet the needs of the stakeholders. Here are some examples of the relationship between QAs and NFRs: QA: The software must be able to handle 1000 concurrent users. NFR: The software must be scalable. QA: The software must be able to recover from a system failure within 5 minutes. NFR: The software must be reliable. QA: The software must be easy to use. NFR: The software must be usable.