The software development life cycle is a structured way to take software from an understood need through design, implementation, testing, deployment and maintenance. It helps make the work and responsibilities visible. It is not simply the period when developers write code, and it does not end when a website or application first goes live.
AWS describes common SDLC activities while noting that process details vary between teams. Use those activities to define useful evidence and ownership for your project rather than treating a methodology name as proof of quality. The business should know what is being accepted at each stage and who owns the software after release. AWS SDLC explanation.
What should discovery and planning produce?
Begin with the business problem, users and complete tasks the software must support. Record existing systems, data sources, constraints and the decisions that remain unresolved. A feature list without the underlying task can make a project appear precise while leaving the intended outcome unclear.
AWS describes planning work such as collecting stakeholder requirements and creating a requirements specification. The useful result is a shared understanding that can be tested later. It should identify what is required, what is excluded and what evidence would show the problem has been addressed.
Do not invent a fixed delivery timetable merely because planning is a lifecycle stage. The plan depends on scope, dependencies and unresolved questions. Keep assumptions visible and update them when evidence changes instead of converting an early estimate into an unsupported commitment.
How do lifecycle models differ in practice?
| Delivery pattern | Useful decision question | Evidence to retain |
|---|---|---|
| Sequential phases | Can the next phase rely on sufficiently stable requirements? | Phase output and unresolved assumptions |
| Iterative delivery | Can a small complete task be tested and improved? | Working increment and feedback |
| Ongoing operational improvement | Who handles defects and changing needs after release? | Issue record, change decision and regression evidence |
AWS discusses models including waterfall and other approaches. This table is an editorial application of the distinction, not a claim that one model guarantees success. A project can use staged approvals while still testing and improving small increments.
The important choice is how uncertainty is handled. If a critical integration is unknown, resolve it through a grounded investigation or prototype rather than postponing it behind a confident implementation schedule.
What should design establish before coding expands?
Design connects the requirements to an implementable solution. AWS includes considering existing modules, technology choices and integration with current infrastructure. That means design can include deciding not to build a component from scratch where an appropriate existing capability meets the need.
Document the records, permissions, interfaces and failure states behind the screens. A visual mock-up can explain a customer journey but does not by itself establish how data is stored or which system owns a transaction. Keep those decisions linked to the requirement they serve.
For a hypothetical approval workflow, show who may request, approve, reject and inspect the result. Define what happens when the request is incomplete or the destination system is unavailable. These are design decisions that affect correctness, not optional details to add after the interface looks finished.
When should testing happen?
AWS describes a combination of automated and manual testing and notes that testing can run alongside development. Do not reserve all checking for a final demonstration after every feature has been assembled.
Choose tests that establish meaningful behaviour. For an order workflow, that may include a valid submission, an invalid input, an interrupted request and a repeated event. Inspect the resulting records and external effects. A successful page load or compilation answers a narrower question than whether the business task works.
Record what was tested and what remains untested. A test result applies to a particular configuration and version. If the implementation changes, identify which evidence needs refreshing rather than carrying forward an old success label automatically. SDLC implementation and testing activities.
An original lifecycle evidence handover
Use the following evidence map throughout the project:
Discovery: user task, current problem and authoritative input sources.
Requirements: acceptance conditions, exclusions and open decisions.
Design: record ownership, permissions, interfaces and failure behaviour.
Implementation: source version, dependencies and configuration instructions.
Testing: cases run, actual results, known defects and affected versions.
Deployment: target environment, release contents and rollback route.
Operation: monitoring owner, incident route and recovery evidence.
Maintenance: change process, dependency updates and continued access to accounts and source.
For each entry, name the role that supplies the evidence and the role that can accept the decision. The map is an original editorial artefact, not a certification framework. It prevents a complete-looking project folder from concealing missing ownership or untested behaviour.
What changes when software reaches production?
AWS distinguishes the copies used for development and testing from the production software customers use. Keep that boundary explicit in the project. Testing should not accidentally send real customer messages, change live records or collect real payments.
Before release, identify the intended configuration and the data migration or setup work required. Preserve the existing state where a change could affect needed records. Define how the team will recognise a failed release and what can be rolled back without losing newer business activity.
A deployment completing successfully is not the same as a working customer journey. Verify representative functions in the target environment using authorised checks and retain the results. Avoid claiming uninterrupted availability or complete security from a single release check.
Who owns maintenance after handover?
Agree who can access the source, hosting, domain, data stores and connected services. Record how defects are reported, how changes are prioritised and who investigates a recovery problem. The business should not discover after launch that essential access belongs only to an unavailable individual.
Separate warranty-like promises from the maintenance work actually agreed. This article does not invent support terms or response commitments. Its purpose is to make those responsibilities explicit before the lifecycle reaches its operational phase.
Discuss a project through Spout’s web development service or start an enquiry with the evidence map and current unresolved decisions.
Record the hosting environment and recovery owner alongside the release evidence.
By Spout Team. Prepared with AI-assisted drafting using the linked primary documentation.