Science & Testing

Testing is applied research into software quality using scientific approaches and evidence to help stakeholders make informed decisions


Defined as value to some person(s), which can include expectations met and positive feelings


Primarily an ability to solve a problem, complete a task or achieve a goal making software a social or cognitive prosthesis or tool

Feelings & Emotions

Focus on people’s feelings as the indicator to quality but be careful to avoid being fooled by illusions


Expectations come from mental models that stakeholders have about the software

Quantitative vs Qualitative

Testing is journaling, note-taking and immersion resulting in a written evaluation of quality carefully supported by metrics where appropriate

Reliability vs Validity

Avoid misleading information by ensuring consistent and accurate testing through diversity of testing methods and approaches

Deduction vs Induction

Testing is constantly building and falsifying hypotheses of quality using logical reasoning to uncover information


Testing scientific model and process of research question, background investigation, analysis, hypothesis, experiment, evaluation, action (reporting)


Quality is evaluated and communicated as stories of context known as events that contain the who, what, where, when, why and how of value to someone

Black vs Clear Box

Testing with and without access or knowledge of software internals to find different problems

Static vs Dynamic

Testing with and without executing the software’s code to find different types of problems

Loss vs Harm

Risk/threat modelling 1: Problems occur when value isn’t deliver or when additional harm occurs

Something vs Nothing

Risk/threat modelling 2: Problems occur when nothing happens when expected but also something happened when unexpected

Test Plan Mind Map

Coverage mindmaps, requirements/risk, charters/questions, designs, environmental/personnel, journaling/noting results