The one topic that seems to be the most prominent one is simply "models".
I would like to start by giving my "favorite" (academic) definition of a model. According to Herbert Stachowiak, a model has at least these three attributes:
- transforming: a model is a transformation of a natural or artificial original (which can be a model by itself),
- reducing: a model does not include all attributes of the original, but only those that seem to be relevant to the model creator or model user
- pragmatic: An original does not have a single, unique model. Models are designed to replace the original
- for certain subjects (who?),
- within a certain time interval (when?), and
- in respect to certain mental or real operations (what for?).
According to Liz Keogh, learning is the process of building (mental) models. When we build models, we delete, distort and/or add information. Based on observed data, we filter the data, generate assumptions, draw conclusions and finally build beliefs. Unfortunately, our beliefs influence our mental filters and thus might constrain our ability to learn. In order to improve our filters we have to adjust our beliefs.
As Esther Derby pointed out, humans are good at spatial reasoning, but bad at temporal reasoning. When we have to analyze temporal problems, we should try to convert them into spatial problems. One option is a graphical model of temporal data, e.g. time lines.
Michael Bolton gave a very good demonstration of the model building process. In the TestLab, he built a model of an application under test through Exploratory Testing. Based on James Bach's "Heuristic Test Strategy Model", he developed a rather comprehensive mind map describing the product and came up with lots of ideas for further testing activities. It became obvious that "Exploratory Testing is simultaneous test design, test execution and learning."
David Evans emphasized that "[test] coverage is [implicitly] measured by reference to models". That is why "you can have complete coverage of a model, but you can never have a complete model". This is due to the reducing nature of any model. And finally David pointed out that "the more complete the model is, the less you can economically cover."
When it comes to complex systems, "there are no clear cause and effect relationships. Accept Uncertainty and complexity. Experiment and measure what really makes things better" (Liz Keogh). You might visualize some relationships using Diagrams of Effects aka. Causal Loop Diagrams (Esther Derby)
Gojko Adzic presented two specific models during the conference: effect maps are a way to model the relationships between software features and a business goal. They can be used to deduce possible activities from a business goal, select the most promising activities and measure their impact on the business.
Gojko started the initiative visualisingquality.org to gather models that facilitate the communication of quality attributes to stakeholders and management. He presented the ACC Matrix that was developed by James Whittaker at Google as one measure.
And What's the Tester's Job?
In a nutshell, testers challenge / break models. They help developers to make more accurate models (Liz Keogh). Thus, model creation is one of the main responsibilities of effective testers.