data

Articles

Woman holding a magnifying glass to her eye Become a Data Detective

Rapid changes in data availability and analysis tools are leading to evolving expectations for the data analyst role. We can do much more than just generate reports; we have the opportunity to not only process data, but convert it into understandable information and use the knowledge revealed by our work to help change happen.

Nels Hoenig's picture Nels Hoenig
Man in a suit reading the Business section of a newspaper Getting Started with Business Intelligence Testing

There’s a bit of hype in terms such as business intelligence, data analytics, and data mining. In testing terms, though, it means working with scripts and databases, often without traditional GUI interaction. But core testing skills—analysis, synthesis, modeling, observation, and risk assessment—will still help you go far in business intelligence testing.

Albert Gareev's picture Albert Gareev
Moving Beyond Basic Load Testing True Performance: Moving Beyond Basic Load Testing

Basic load testing is valuable, but it's important to move past simplistic efforts. Here are some ways to gain more accurate metrics from your load tests.

Jim Holmes
2012: The Year of DevOps

Scott Ambler explains how DevOps has grown within the agile community, and why he believes it will become an IT buzzword in 2012. DevOps uses agile's community-based teamwork and offers developers and those in operations a great way to make everyone's job easier.

Scott W. Ambler's picture Scott W. Ambler

Better Software Magazine Articles

Good Tool. Bad Application

Excel does a great job as a spreadsheet, but when you try to push it into service as a database you may be in for some rude surprises.

Chris McMahon's picture Chris McMahon

Interviews

Geoff Meyer Analytics, Data, and How Testing Is like Baseball: An Interview with Geoff Meyer
Video

In this interview, Geoff Meyer, a test architect in the Dell EMC infrastructure solutions group, explains how test teams can succeed by emulating sports teams in how they collect and interpret data. Geoff explains how analytics can better prepare you for the changing nature of software.

Jennifer Bonine's picture Jennifer Bonine
Daria Mehra Machine Learning and Artisanal Testing: An Interview with Daria Mehra
Video

In this interview, Daria Mehra, the director of quality engineering at Quid, explains how people can use machine learning to better contextualize data, details the complexity of test automation and how to be sure you have enough test coverage, and defines the term “artisanal testing.”

Jennifer Bonine's picture Jennifer Bonine
Alon Eizenman Testing with the Lights On: An Interview with Alon Eizenman
Video

In this interview, Alon Eizenman, the CTO and cofounder at SeaLights Technologies, discusses his many experiences with startup companies, how software teams are adapting to the current demand for speed, and why you need data before you take testing actions.

Jennifer Bonine's picture Jennifer Bonine
Cher Fox Why Test Automation Is Important for Agile Data Teams: An Interview with Cher Fox

In this interview, Cher Fox, of Fox Consulting, explains why test automation is essential for agile data teams' success. However, there are many other items to consider and address before implementing test automation. You may be able to get started with tools you already have.

Josiah Renaudin's picture Josiah Renaudin

Conference Presentations

Data in Functional Testing-You Can't Live Without It

This paper sets out to illustrate some of the ways that data can influence the test process, and will show that testing can be improved by a careful choice of input data. In doing this, the paper will concentrate most on data-heavy applications; those which use databases or are heavily influenced by the data they hold. The paper will focus on input data, rather than output data or the transitional states the data passes through during processing, as input data has the greatest influence on functional testing and is the simplest to manipulate. The paper will not consider areas where data is important to non-functional testing, such as operational profiles, massive datasets and environmental tuning.

James Lyndsay, Workroom Productions

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