Reliability as a First-Class Software Engineering Requirement

In modern software systems, reliability is no longer a downstream operational concern—it is a foundational software engineering requirement. As organizations increasingly rely on distributed, cloud-native platforms to deliver mission-critical services, the cost of unreliable software has shifted from inconvenience to existential risk. Outages today can halt financial transactions, disrupt supply chains, and erode user trust within minutes. In this environment, treating reliability as an afterthought is no longer sustainable.

Industry discussions within the IEEE Computer Society have increasingly emphasized reliability as a core system design concern rather than an operational afterthought (IEEE Computer Society Tech News).

Traditionally, software engineering emphasized functionality and performance, while reliability was delegated to operations teams through monitoring and incident response. This separation worked reasonably well in monolithic systems, where failures were easier to localize and control. However, in microservice-based architectures composed of independently deployed services, reliability must be engineered into the system from the very first design decision. Each service interaction introduces new failure modes, and without deliberate reliability engineering, complexity compounds rapidly.

Reliability Begins at Design Time

Making reliability a first-class requirement means embedding failure awareness into software design. Engineers must assume that components will fail—networks will partition, dependencies will become unavailable, and workloads will spike unpredictably. Design patterns such as circuit breakers, bulkheads, retries with exponential backoff, and idempotent APIs are no longer optional enhancements; they are essential engineering primitives.

Equally important is defining explicit service-level objectives (SLOs) during the design phase. Rather than optimizing solely for feature velocity, teams must design systems around measurable reliability targets such as availability, latency, and error budgets. These objectives provide a shared contract between development and operations, ensuring that reliability trade-offs are intentional and transparent.

Figure 1. Conceptual categories of modeling approaches used to engineer reliability into modern software systems, ranging from statistical methods to advanced machine-learning and hybrid techniques.

Observability as a Software Engineering Discipline

Reliability cannot be sustained without deep visibility into system behavior. Observability—through metrics, logs, and distributed traces—has evolved into a core software engineering discipline rather than an operational add-on. Instrumentation must be designed alongside application logic, enabling engineers to understand how systems behave under normal and failure conditions.

Modern observability enables teams to move beyond reactive alerting toward proactive diagnosis and continuous improvement. By correlating traces across service boundaries and analyzing real-time telemetry, engineers can identify systemic bottlenecks, detect cascading failures early, and validate whether reliability goals are being met in production. This feedback loop is essential for building resilient systems at scale.

Automation and Resilience at Runtime

Treating reliability as a first-class requirement also demands automation. Manual intervention does not scale in highly distributed environments. Cloud-native platforms now enable automated recovery through self-healing mechanisms such as auto-scaling, health-based restarts, and event-driven remediation workflows. Increasingly, AI-driven analytics are being integrated into these pipelines to detect anomalies and optimize responses in real time.

However, automation alone is insufficient without disciplined engineering practices. Automated systems must be tested rigorously through fault injection and chaos experiments to ensure they behave as expected under stress. Reliability engineering thrives when failure is treated as a learning opportunity rather than an exception to be avoided.

Figure 2. Observability-driven reliability control loop illustrating how telemetry, policy-driven decisions, and automated remediation work together to maintain system stability at runtime.

A Cultural Shift in Software Engineering

Ultimately, elevating reliability to a first-class requirement requires a cultural shift. Software teams must move from a mindset of “build and deploy” to one of “design, observe, and evolve.” Practices such as blameless postmortems, reliability-focused code reviews, and cross-functional ownership help embed reliability into everyday engineering workflows.

As software systems continue to grow in scale and societal impact, reliability will increasingly define engineering excellence. Organizations that recognize reliability as a core software engineering responsibility—not merely an operational concern—will be better positioned to build systems that are trustworthy, resilient, and capable of evolving in an unpredictable world.

About the Author

Muzeeb Mohammad is a Senior Manager of Software Engineering at JPMorgan Chase, Senior Member of IEEE, and Fellow of the Institution of Electronics and Telecommunication Engineers (IETE). He specializes in the design and delivery of secure, resilient, and highperformance distributed microservices for largescale financial systems, with a strong emphasis on cloud-native architectures, event-driven platforms, ZeroTrust security, and AI-augmented reliability engineering.

Disclaimer: The authors are completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.