About this episode
Summary
Quality assurance in the software industry has become a shared responsibility in most organizations. Given the rapid pace of development and delivery it can be challenging to ensure that your application is still working the way it’s supposed to with each release. In this episode Jonathon Wright discusses the role of quality assurance in modern software teams and how automation can help.
Announcements
- Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
- When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Your host as usual is Tobias Macey and today I’m interviewing Jonathon Wright about the role of automation in your testing and QA strategies
Interview
- Introductions
- How did you get introduced to Python?
- Can you share your relationship with software testing/QA and automation?
- What are the main categories of how companies and software teams address testing and validation of their applications?
- What are some of the notable tradeoffs/challenges among those approaches?
- With the increased adoption of agile practices and the "shift left" mentality of DevOps, who is responsible for software quality?
- What are some of the cases where a discrete QA role or team becomes necessary? (or is it always necessary?)
- With testing and validation being a shared responsibility, competing with other priorities, what role does automation play?
- What are some of the ways that automation manifests in software quality and testing?
- How is automation distinct from software tests and CI/CD?
- For teams who are investing in automation for their applications, what are the questions they should be asking to identify what solutions to adopt? (what are the decision points in the build vs. buy equation?)
- At what stage(s) of the software lifecycle does automation live?
- What is the process for identifying which capabilities and interactions to target during the initial application of automation for QA and validation?
- One of the perennial challenges with any software testing, particularly for anything in the UI, is that it is a constantly moving target. What are some of the patterns and techniques, both from a developer and tooling perspective, that increase the robustness of automated validation?
- What are the most interesting, innovative, or unexpected ways that you have seen automation used for QA?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on QA and automation?
- When is automation the wrong choice?
- What are some of the resources that you recommend for anyone who wants to learn more about this topic?
Keep In Touch
Picks
Links
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Dec 2022
Update Your Model's View Of The World In Real Time With Streaming Machine Learning Using River
Preamble
This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
Summary
The majority of machine learning projects that you read about or work on are built around batch processes. The model i ... Show More
1h 16m
Dec 2022
Declarative Machine Learning For High Performance Deep Learning Models With Predibase
Preamble
This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
Summary
Deep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference ... Show More
59m 22s
Nov 2022
Build Better Machine Learning Models With Confidence By Adding Validation With Deepchecks
Preamble
This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
Summary
Machine learning has the potential to transform industries and revolutionize business capabilities, but only if the mo ... Show More
47m 37s
Jul 2025
Revolutionizing Python Notebooks with Marimo
SummaryIn this episode of the Data Engineering Podcast Akshay Agrawal from Marimo discusses the innovative new Python notebook environment, which offers a reactive execution model, full Python integration, and built-in UI elements to enhance the interactive computing experience. ... Show More
51m 56s
Feb 2025
#495: OSMnx: Python and OpenStreetMap
See the full show notes for this episode on the website at <a href="https://talkpython.fm/495">talkpython.fm/495</a>
1h 1m
Oct 11
Context Engineering as a Discipline: Building Governed AI Analytics
SummaryIn this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Nick Schrock, CTO and founder of Dagster Labs, to discuss Compass - a Slack-native, agentic analytics system designed to keep data teams connected with business stakeholders. Nick shares his j ... Show More
51m 58s
Sep 2021
An Exploration Of The Data Engineering Requirements For Bioinformatics
<div class="wp-block-jetpack-markdown"><h2>Summary</h2>
<p>Biology has been gaining a lot of attention in recent years, even before the pandemic. As an outgrowth of that popularity, a new field has grown up that pairs statistics and compuational analysis with scientific research ... Show More
55m 10s
Aug 26
From Academia to Industry: Bridging Data Engineering Challenges
SummaryIn this episode of the Data Engineering Podcast Professor Paul Groth, from the University of Amsterdam, talks about his research on knowledge graphs and data engineering. Paul shares his background in AI and data management, discussing the evolution of data provenance and ... Show More
50m 54s
May 2022
Insights And Advice On Building A Data Lake Platform From Someone Who Learned The Hard Way
<div class="wp-block-jetpack-markdown"><h2>Summary</h2>
<p>Designing a data platform is a complex and iterative undertaking which requires accounting for many conflicting needs. Designing a platform that relies on a data lake as its central architectural tenet adds additional la ... Show More
58m 11s
Aug 18
High Performance And Low Overhead Graphs With KuzuDB
SummaryIn this episode of the Data Engineering Podcast Prashanth Rao, an AI engineer at KuzuDB, talks about their embeddable graph database. Prashanth explains how KuzuDB addresses performance shortcomings in existing solutions through columnar storage and novel join algorithms. ... Show More
1h 1m
Mar 2021
Data Quality Management For The Whole Team With Soda Data
<div class="wp-block-jetpack-markdown"><h2>Summary</h2>
<p>Data quality is on the top of everyone’s mind recently, but getting it right is as challenging as ever. One of the contributing factors is the number of people who are involved in the process and the potential impa ... Show More
58 m
Aug 2024
The Evolution of DataOps: Insights from DataKitchen's CEO
Summary
In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Chris Berg, CEO of DataKitchen, to discuss his ongoing mission to simplify the lives of data engineers. Chris explains the challenges faced by data engineers, such as constant system failures ... Show More
53m 30s
Feb 2025
The Future of Data Engineering: AI, LLMs, and Automation
Summary
In this episode of the Data Engineering Podcast Gleb Mezhanskiy, CEO and co-founder of DataFold, talks about the intersection of AI and data engineering. He discusses the challenges and opportunities of integrating AI into data engineering, particularly using large langua ... Show More
59m 39s