Category Archives: Quality Management

The Software Quality Engineering Leader

4 P’s and a T

I frequently describe the role of a Quality Engineering Leader as 4-P’s and a T.  The 4 P’s are People, Product, Process, Project, and the T stands for Technology. This is a good prompt to write it down.

I’ve developed this model leading quality engineering teams in several Silicon Valley organizations, which lead to several common elements in the context.  We are building software, services, and products in a competitive market, servicing many customers. We use agile development methodologies and iterative releases. Our focus is delivering high quality, at speed. To accomplish these goals, we need to build quality in rather than test it in. We believe that prevention and finding issues early is better than finding them late.

The Quality Engineering Leader is a close partner with the development leader, the product owner, and customer support team. The Quality Engineering Leader is someone who is passionate about delivering great quality outcomes to our customers. They will bring an engineering mindset – which means to help build quality in at all stages of the Software Development Life-cycle. They will also be a strong leader, with the influence to paint a vision of quality which leads to change in teams that are not necessarily in their direct control.

The Quality Engineering leader demonstrates a balance across several dimensions:

People Leadership: Able to attract and recruit strong Quality Engineers, and help them be the best that they can be professionally. Help deploy the right people to the projects, so the projects are successful, while finding the right projects for each person to help them develop their career.

Product Advocacy: Understand how our products improve our customer’s lives & businesses – and help the team build the right offering in addition to building it right. Being the customer advocate in the development squads helps us build the products that our customers love.

Process Leadership: Be current on the latest quality engineering practices and be able to apply the right practice to our situation. Have a well thought out strategy for when to automate, how to automate, where to automate, and what is best left to the humans. Another aspect of process leadership is to help the engineering teams repeat success again and again instead of relying on heroics.

Project Management: Organize our work to focus on the most important items, and be transparent to our stakeholders. . Help the wider team make the necessary trade-offs between time, features, and investment. Track progress of the work and resolve the inevitable issues that pop up in every project.

Technology Focus: Able to understand our technologies sufficiently to lead an engineering team, helping the team make the best decisions when it comes to technology, and ask the right questions. Stay current on the emerging technologies and platforms that are important to our products.

The typical front-line leader will be solid in 2-3 of these dimensions and developing/growing in the balance.

This is an edited version of my LinkedIn article with the same title.

Software Root Cause Analysis: 3 Questions to Answer

Image of explosion represents things going wrong on a project.

Sometimes, things don’t go as planned.

Here are three questions that I like to answer when performing a root cause analysis for escaped bugs:

  1. How was the bug introduced in the first place?
  2. How did we not catch it earlier?
  3. What are we doing to prevent this problem in the future?

For the first two questions, I have a handy template for performing root cause analysis.

Generally, for the 3rd question, what are we doing to prevent the problem, we have short and longer term solutions.  In the short term, we should add the appropriate test or check that missed the problem in the first place.  That is the answer to the specific question for that particular issue.

For the longer term, we collect data about the escaped causes and reasons for escape.  We collect that data in the bug tracking system as two fields with categories.  When we have enough data, we can examine trends.  I usually start with a simple Pareto analysis, showing the top few causes/reasons. Then work with the team to ask how can we improve our processes/practices.  Its often useful to filter the Pareto analysis to the most painful bugs (those found by customers, high severity, etc.)

Please drop a comment below and let me know what you do for root cause?

By Photo courtesy of National Nuclear Security Administration / Nevada Site Office [Public domain], via Wikimedia Commons

Vanity Testing Metrics

This is a preview of a topic that I will cover in the upcoming talk, Testing Metrics – Choose Wisely at STPCon.

Vanity metrics are popular in marketing. These are metrics that allow you to feel good, but aren’t directly actionable, and are not related to your (true) goals.  Vanity metrics are also easily manipulated.  An example would be a hit counter, measuring page views, on a web site.  What would really matter for a business web site would be the conversion rate (how many visitors actually purchase) or revenue per customer.

I’ve seen marketing campaigns that add a lot of page views, but actually cause a decrease in conversion rate. The advertising may find more viewers, but if the people are less interested in your product, its not really useful to drive up traffic.   (and who knows if those viewers are really people and not bots) Measuring the impact of advertising by measuring revenue or number of visitors that become customers is more powerful.

An example in software testing is measuring the Average Age of bugs.  You might start a campaign to reduce bug backlog or improve the velocity of fixing the bugs, and a measure might be the average age.  However, what you are really looking for is a quicker response to every bug, not the average bug.

The average age of bugs chart from JIRA shows trends in the average age, over time.

The average age of bugs chart from JIRA shows trends in the average age, over time.

This metric is often misleading in these efforts, as really old bugs can be fixed or closed and dramatically reducing the average age.  In the chart above,  the dramatic downward swings actually came from closing only a couple of bugs. Those bugs weren’t fixed, they were closed as obsolete.  But, they were open in the backlog for several years, so closing them had a dramatic impact on the average age.  Closing those, however, didn’t tell us anything about the responsiveness to current bugs.

Instead of Average age, tracking the median age.   The median measure would be much less affected by really old bugs.  Medians are a way to prevent outliers in having outsized impact on your metrics.  Even better, a more direct measure of our goal to improve velocity might be to set a target timeframe, say 30 days – then measure the percentage of bugs that are fixed within that target.

These views will more directly measure your goal (improved velocity) and be less susceptible to manipulation.

 

Eliminating biases in A/B testing

A/B testing is a powerful customer-driven quality practice, which allows us to test a variety of implementations and find which works better for customers.  A/B testing provides actual data, instead of the HIPPO.

The folks at Twitch found that the users in the test cell had higher engagement than the control group. They found that this higher engagement came from factors other than the new experience, which might cause a cognitive bias in their results.  Factors like the Hawthorne effect and new users break the randomness for the experiment.

They adjusted the data to reduce the impact of these effects, and provided a great case study on how they did it

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