Oleksandr said: My hot take of the week: If you don't fully understand the thing you want to test and measure - you might get the results that are surprising but yet mysterious. So - first - unders...

The question of who actually benefits from a productivity gain. In an AI context, if a developer produces ten times more output using AI tools, value capture describes whether that surplus flows to the employer (in the form of more work for the same pay), to the employee (in the form of reduced hours for the same pay), or is divided somewhere in between. The term frames productivity improvements not as neutral gains but as contested resources, with company and worker pulling in opposite directions over who keeps the benefit.

A metaphor for the way AI-assisted work drains human energy without commensurate reward. When workers adopt AI tools that dramatically increase output, but employers absorb most or all of the productivity gain, the worker is left exhausted with nothing to show for it. The term draws on the "energy vampire" archetype: proximity to the source is what depletes you, not direct harm. Common manifestations include compulsive overworking driven by the addictive feedback loop of agentic coding tools, and organisational pressure to sustain AI-boosted output as a new baseline indefinitely.

I've been in testing (and quality and tech) for over 25 years. That's wild.I went through a lull period, questioned whether it was for me, got bored and I struggled with the lack of progress and th...

Rahul Parwal and Rosie Sherry discuss how quality professionals can navigate AI adoption, avoid the noise, and find where human skill still matters most.

The inputs, values, and system state used to execute a test case and verify that software behaves as expected. Test data spans a wide range of forms: valid inputs that exercise the happy path, invalid or boundary inputs that probe edge cases, and pre-existing system state such as user accounts, tokens, or database records that a test depends on. In automated testing, managing test data carefully is critical: each test should operate against a known, isolated set of data, use explicit fixtures or seeded records rather than shared or assumed state, and clean up after itself to avoid polluting subsequent runs. AI-generated automation scripts frequently omit proper test data setup and teardown, making this one of the key areas a human engineer must review and strengthen before the code is considered framework-ready.

A gradual, often unnoticed divergence between the configuration of different environments (such as development, staging, and production) that causes tests to behave differently depending on where they run. Config drift is one of the common causes of environment-related test failures, and a key cluster to look for when triaging automated test results. It can manifest as a service that behaves differently in CI than locally, a feature flag set differently across environments, or a third-party dependency pointing to a different endpoint.

The practice of framing instructions or context given to an AI tool in a way that shapes the quality and relevance of its output. In a testing context, how a requirement or instruction is written for an AI tool significantly affects what test cases or automation code it generates. Rather than simply asking an AI to write test cases, effective prompting names the risks to surface, specifies the types of tests needed (positive, negative, boundary), and provides enough context about the system under test for the AI to produce targeted, useful output. Poor prompting produces plentiful but shallow results; well-structured prompting produces focused, risk-aware coverage.

The externally visible outcome of a system action that a test can verify, as distinct from internal implementation details. In test automation, assertions should check observable behaviour rather than internal state: for example, checking that a specific error message appears on screen, or that a button becomes visible after a reset, rather than checking a variable value inside the code. AI-generated assertions sometimes check implementation details that are brittle or invisible to the user, so reviewing whether each assertion targets a genuine observable outcome is one of the key disciplines when reviewing AI-produced automation code.

Test data that is explicitly inserted into a system or database before a test runs, in order to establish a known, controlled state for the test to operate against. Seeding ensures tests are repeatable and do not rely on pre-existing or shared data that may change between runs. For example, a login test might seed a user account with a specific username and password before executing, then clean up that account afterwards. Seeded data is a key discipline in robust test automation and something AI-generated scripts frequently omit, requiring a human to add it.

A judgement about whether a piece of software is sufficiently tested, stable, and low-risk to be deployed to users. Release readiness takes into account test coverage, outstanding defects, severity of known issues, risk exposure, and stakeholder confidence. It is one of the decisions that is often explicitly identified as remaining human responsibility and not something that should be delegated to AI, because it requires contextual knowledge of the business, the users, and the acceptable risk threshold that an AI tool cannot reliably supply.

The deliberate management of the data used in automated tests, including how it is created, seeded, isolated per test, and cleaned up after execution. Good test data control ensures that each test runs against a known, predictable state and does not depend on or pollute shared data. Approaches include explicit fixtures (pre-defined data sets loaded before a test), seeded data (data inserted directly into a database or system before the test runs), and cleanup passes (removing test-generated data after execution). AI-generated automation code often lacks proper test data control, making it one of the key areas a tester must review and strengthen before committing scripts to a CI/CD pipeline.