We are developing an empirically validated decision support system (DSS) for selecting a tool for Software Testing / Software Test Automation. The DSS is based on the concept of “Wisdom of the crowd”.
The survey will take about a few minutes for each tool you prefer to evaluate. We appreciate your time.
The survey consists of:
- 9 short questions about background information
- 15 criteria for evaluating Robot Framework.
- You may also evaluate other tools, if you wish to do so.
After submitting your response you will receive feedback how the tool(s) have been evaluated by others.
Your response is valuable and will help us a great deal!
Thank you in advance!
Background: This survey is in a form of a web-tool. The survey is based on a data from software professionals, from a preliminary survey conducted in the industry in the spring of 2016. The survey has been re-run again in 2017 among software practitioners. Now we are collecting feedback from the true users of Robot Framework – from those who have experience either with using or developing the tool – or both. The results of the research will yield an empirically validated DSS.
Essentially, we are using the wisdom of the crowd in exploring the question “What is the Best Tool to support Software Testing and Test Automation for a particular purpose or context” but with a primary focus on the Robot Framework.
Wisdom of the Crowd
The concept is defined in Wikipedia as “The Wisdom of the crowd is the collective opinion of a group of individuals rather than that of a single expert. A large group’s aggregated answers to questions involving quantity estimation, general world knowledge, and spatial reasoning has generally been found to be as good as, and often better than, the answer given by any of the individuals within the group.” (Wikipedia)
The data collected from this survey and from the resulting DSS can be used for research purposes only.
All responses have been and will be handled anonymously.
University of Oulu, UBICOMP & M3S research groups.
Take the Survey Below: