LoadRunner Analysis Tips and Tricks

I was looking to put together a list of LoadRunner Analysis reporting tips and tricks for our company Wiki. It didn’t take long however to find a comprehensive list by Nick Wilton.

I highly recommend taking a look at this list, and I have highlighted my top 5 tips below. To read the rest of Nick’s tips, or to leave him a thank you message, check out the full article over at JDS Australia here.

1. Tell a Story

All graphs you present via the Analysis tool should tell a story. Performance Testing is there to answer the question of whether the application will scale to the load level required in production…and as a performance analysis you should produce graphs to provide evidence of either the success or failure of this performance testing.

2. Set your Y-Axis to be the same for all graphs

Graphs should be easily comparable and “tell the same story”. By default the Y-Axis is set to Automatic, which sets the minimum and maximum scale to match your data. Go to Display Options > Advanced > Axis tab, and change Minimum and Maximum values from “Auto” to something sensible. Percentage graphs should always be ranges from 0% to 100%.

As a general rule, your Y-Axis scale should always start at zero and your ideal or SLA level should be approximately one-third up the scale. People we automatically assume response times in the lower third of your graph are “good” and the upper two-thirds are “poor”.

3. Make use of Templates

This feature (Tools > Templates) allows you to quickly apply the same graphs and formatting from one Analysis scenario to another. This is great for comparing results, however always double-check filters to after applying a template to ensure you are not filtering out any important data.

4. Focus on just a few transactions.

Performance tests can have sometimes hundreds of transactions and this can generate some very busy graphs. Decide on 3-5 transactions that have the highest business value or are of particular interest…focusing your analysis on just a few transactions is more valuable to the readers of your report.

5. Setting the Granularity

The granularity affects the graph smoothing, or number of data points. A high granularity value will reduce clutter and improve readability, but it may hide events such as spikes. The trick is to adjust the granuarity to maximise readability but accurately represent any important events. Caution, however for larger performance tests setting the granularity too low may cause the Analysis tool to hang for a few minutes.

It is also important to note that Analysis recalculates the average response times when the granularity is changed, so be sure to always use the same granularity when comparing test runs.