- From The Pipeline v36.0
- From the Pipeline v9.0
- From the Pipeline v15.0
- From the Pipeline v14.0
- From the Pipeline v13.0
- From the Pipeline v12.0
- From the Pipeline v11.0
- From the Pipeline v10.0
- From the Pipeline v8.0
- From the Pipeline v17.0
- From the Pipeline v7.0
- From the Pipeline v6.0
- From the Pipeline v5.0
- From The Pipeline v4.0
- From the Pipeline v3.0
- From the Pipeline v2.0
- From the Pipeline v16.0
- From the Pipeline v18.0
- From The Pipeline v35.0
- From The Pipeline v28.0
- From The Pipeline v34.0
- From The Pipeline v33.0
- From The Pipeline v32.0
- From The Pipeline v31.0
- From The Pipeline v30.0
- From The Pipeline v29.0
- From the Pipeline v27.0
- From the Pipeline v19.0
- From the Pipeline v26.0
- From the Pipeline v25.0
- From the Pipeline v24.0
- From The Pipeline v23.0
- From the Pipeline v22.0
- From the Pipeline v21.0
- From the Pipeline v20.0
- From the Pipeline v1.0
The following will be a regular feature where we share articles, podcasts, and webinars of interest from the web.
A Primer on Engineering Delivery Metrics
Juan Pablo Buriticá recently published on excellent article on engineering metrics. The focus of the article is learning about what and how to measure the software delivery phase of development. The first step is to define why you want to measure something — look for the outcome. Another key component is building trust in the organization, so the team believes in the strategy. Some of the Software Delivery Performance Metrics to consider: delivery lead time, deployment frequency, mean time to restore, and change failure rate.
How to Start Testing with Python
This webinar led by Andy Knight walks you through the essentials of test automation with Python. He uses pytest as the framework. During the course of the session, he shows how to write unit & integration tests. He also gives a rundown of parameters, fixtures, and plugins.
What Are Machine Learning Uses to Improve Static Analysis
The article demonstrates a few use cases for machine learning in performing static analysis of defects. For one, it can be used to group defects together that are similar in nature. These groupings can be used to look for patterns in system behavior. Another usage is ranking defects based on how straightforward or complex they are; AI-assisted defect ranking uses supervised learning. A similarity score is attached to defect report to either “True Positive Reports” (TP) or “False Positive Reports” (FP). The two groups are based on the results of prior review of the defects reported in the past.
Improving Test Data Collection and Management
“There is much published about the data we generate to assess product quality. There is less discussion about the data testers generate for our own use that can help us improve our work—and even less is said about recommended practices for data collection. Test data collection, management, and use all call for upfront planning and ongoing maintenance. Here’s how testers can improve these practices.”
Book Review: Explore it!
Kristin Jackvony has posted a review of Elizabeth Hendrickson’s “Explore It!” book on exploratory testing. The book should be required reading for anyone in software testing. The first key delineation; checking is what a tester does when they want to make sure that the software does what it’s supposed to do, whereas exploring is what a tester does when they want to find out what happens if the user or the system doesn’t behave as expected.