
The Forward-Deployed Engineer Needs a New Operating Model for 2027
Discover how a new forward-deployed engineering model helps enterprises modernize legacy systems, govern AI agents, and deliver production-ready AI at scale.
The term “Exploratory Testing” evolved in the past years, software testing experts nowadays discuss the changes in its conception and evolution. James Bach and Michael Bolton described the timeline as a stairway with precise stages: ET 1.0 (Rebellion), ET1.5 (Explication), ET2.0 (Integration) and finally ET3.0 (Normalization).
It’s easy to comprehend the first two steps as a rebellion of the imperative conception of testing and the way through defining its meaning as a separated technique.
Integration leads to a continuum flux between the scripted testing and its exploratory sibling we have been experiencing in the last decade. This continuum fills in the gap, understanding the need for modeling reality in order to describe the behavior of the software and figuring the exploratory elements required to do so. On the other hand, if we are only applying exploratory testing, in our mental models there are many concepts already incorporated, “formalized” by our knowledge and experiences as testers; consciously or unconsciously we “plan” our test strategy.
Also read: PTaaS – Penetration Testing As A Service
The step of Normalization is nothing less than the deprecation of the term “exploratory testing”. There’s no need to use the term “exploratory testing” because the essence of software testing is eminently exploratory and we should conceive activities like test case design, test automation at different levels, verification checklists, among others, as components and tools that assist the tester in the activity.
This leads to an open debate because this new conception of testing takes special significance in the context of agile methodologies. We are glad to hear your thoughts about it ???? Leave us a comment below! How is your conception of ET evolving?

Discover how a new forward-deployed engineering model helps enterprises modernize legacy systems, govern AI agents, and deliver production-ready AI at scale.
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