D787 is a graduate course in WGU's M.S. Software Engineering, Domain Driven Design plan.
WGU describes it as preparing students to analyze real-world systems and make informed decisions that improve design, performance and reliability, including creating tests that validate both scalability and reliability.
The central habit is evidence before action. Students often jump to a fix, such as adding servers or caching, before identifying where the system is actually constrained. The course rewards a disciplined cycle: measure, find the bottleneck, change one thing, and measure again. The guide lists three competency units.
Course at a Glance
| Item | Details |
|---|---|
| University | Western Governors University (WGU) |
| Course code | D787 |
| Level | Graduate (3 competency units in the guide) |
| Program | M.S. Software Engineering, Domain Driven Design |
| Subject area | Performance, scalability and reliability testing |
| Typical work | A written analysis with a test plan for a system scenario |
What D787 Covers
| Competency (summarized) | What it means in practice |
|---|---|
| Analyze the health and performance of a system | Use metrics and logs to find and document issues |
| Apply scalability optimization techniques | Help the business handle rising and falling workloads |
| Apply theory to real systems | Use concepts to make informed design and reliability decisions |
| Create performance tests | Validate that the system scales and stays reliable |
Key Concepts Explained
Bottlenecks and Measurement
A system is as fast as its slowest resource. Useful metrics include response time, throughput, error rate and resource use such as CPU, memory and database connections. Percentile response times often reveal problems that an average hides.
Example: Average response time is 300 ms, but the slowest few percent of requests take several seconds. Tracing those requests shows a database query without an index; adding the index fixes the slow tail, which more servers would not.
Scaling up and Out
Vertical scaling adds power to one machine; horizontal scaling adds more machines behind a load balancer. Horizontal scaling suits stateless services and sudden demand, but needs shared state to be handled carefully through caches, databases or queues.
Example: A web tier can scale out automatically at peak hours and shrink afterwards, while the database is protected by caching read-heavy queries.
Test Types
Load tests apply expected traffic, stress tests push past limits to find failure points, spike tests apply sudden surges and soak tests run for long periods to expose leaks. A good test plan states the goal, workload, success thresholds and how results will be read.
Typical Assignments and How to Approach Them
| Task element | What it tests | How to approach it |
|---|---|---|
| System health analysis | Reading metrics | Present data, name the bottleneck and say how you know |
| Scalability plan | Choosing techniques | Match each technique (caching, scaling out, queues) to an identified constraint |
| Test design | Validating scale and reliability | State test type, workload, thresholds and expected outcome |
| Recommendations | Evidence-based decisions | Link each change to a measurable improvement target |
A Worked Test Plan Layout
A test plan that reads clearly will usually be scored clearly. Use one row for each test and keep the thresholds measurable.
| Test | Workload | Success threshold |
|---|---|---|
| Load | Expected peak concurrent users | 95th percentile response under a stated limit |
| Stress | Increase until failure | Failure point recorded and recovery observed |
| Spike | Sudden surge from baseline | Autoscaling responds within a stated time |
| Soak | Steady load for hours | No memory growth or rising errors |
After the table, explain what you would change if a threshold fails, and how you would retest. That closing loop shows the data-driven approach the course emphasizes.
Readiness Checklist Before You Submit
Because the course turns on evidence, run through this list before you submit.
| Check | What to confirm |
|---|---|
| Metrics shown | Response time, throughput, error rate and resource use are presented, not just described |
| Bottleneck named | One constraint is identified and supported by the data |
| Tests defined | Each test has a workload and a measurable threshold |
| Change linked to evidence | Every recommendation points to a measurement |
| Retest planned | You say how the effect will be checked |
Where Students Get Stuck
- Fixes before evidence. Recommendations should follow from the measurements you present.
- Averages only. Include percentile or worst-case figures when describing user experience.
- Scaling without state. Explain how sessions and data behave when instances multiply.
- Tests with no thresholds. Without a pass condition a test cannot validate anything.
Study Tips for D787
- Memorize the four test types and one sentence on what each reveals.
- Practice reading a latency graph and naming the likely bottleneck.
- Build a template for recommendations: problem, evidence, change, expected effect, retest.
- Review D786 notes on service boundaries, since scaling decisions often follow them.
How We Help with D787
Send the scenario, any data provided, the rubric and your draft. A writer with a performance engineering background can prepare a custom worked analysis for study, explain test design, or review your own plan for evidence gaps. Our master's assignment help guide describes the process.
GradeEssays is independent of Western Governors University. Model work is for study and reference; your submission must be your own under WGU's academic integrity policy, and no result is promised. Orders are written from scratch with free revisions within the scope of your original request, and a full refund if late.
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Frequently Asked Questions
WGU's guide lists it in the M.S. Software Engineering, Domain Driven Design plan.
Three, according to the guide.
Performance is how fast a system responds under a given load; scalability is how well it keeps performing as the load grows.
Load, stress, spike and soak testing are the standard set.
Learn what tools in the category do; follow your task instructions for whether you must run any.
Tie each one to evidence, a predicted effect and a retest plan.