{
  "id": 16098353,
  "title": "The Regression Tests Nobody Writes: Capturing How Software Actually Behaves",
  "url": "https://urgent.news/2026/10/22/the-regression-tests-nobody-writes-capturing-how-software-actually",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-22T19:37:29.000Z",
  "source": {
    "name": "Stack Overflow Blog",
    "slug": "stack-overflow-blog",
    "url": "https://stackoverflow.blog/2026/10/22/the-regression-tests-nobody-writes-capturing-how-software-actually-behaves/"
  },
  "original_language": "en",
  "account": "The question often arises whether tests in a codebase accurately reflect the system's actual behavior or merely represent what someone intended. While well-written tests are essential, they typically focus on what someone thought the system should do, rather than what it actually does in practice. In a system operating for a few years, new behaviors emerge that were never anticipated or documented, often leading to hidden quirks and edge cases. These can depend on various factors, such as default values, error messages, response times, and even the appearance or absence of fields. When a system gains widespread usage, every observable behavior is eventually discovered and relied upon by some user or service, making hidden dependencies a significant source of unexpected regressions. Hyrum's Law, coined by Hyrum Wright at Google, highlights this phenomenon: as more people use an API, the system ends up depending on all observable behaviors, not just the documented contract. This realization should prompt developers to capture the system's actual behavior through tests that go beyond traditional expectations. These tests, often referred to as characterization tests, function by recording what the system does when run, without aiming to correct or improve that behavior. Instead, the goal is to serve as a safeguard that alerts developers when the behavior changes unexpectedly. While writing characterization tests can be labor-intensive, recent approaches like golden master or approval testing have made the process more manageable. Golden master testing involves capturing a full output of a function or service once, saving it as a reference, and subsequently comparing each subsequent output to this reference. Frontend snapshot tests apply a similar approach, capturing visual aspects of a user interface. However, capturing the behavior of services is more complex, as it involves more than just the return values - it includes how the service reacts to requests, its internal operations, interactions with other services, and its side effects. Real-world traffic provides an excellent source of characterization data, as each real request and response effectively acts as an unscripted characterization test. By recording all incoming requests and outgoing responses, developers can create a comprehensive characterization suite that reflects the system's behavior under real-world conditions. This includes recording database queries, external service interactions, and any other side effects. By replaying these recordings later with dependencies satisfied by the same captured data, developers can ensure that the system behaves consistently across different runs. An essential aspect of this characterization approach is understanding that a red characterization test doesn't necessarily indicate a bug. Instead, it signals a change in behavior that needs to be evaluated - is this a deliberate change that aligns with the system's evolving needs, or is it an unintended regression? By treating these changes as decisions rather than arbitrary bugs, teams can foster a culture of continuous review and improvement, where behavior changes are discussed and documented, rather than silently occurring and potentially causing user frustration. In essence, leveraging real-world traffic data to create a comprehensive characterization suite helps teams catch regressions that traditional tests might miss, transforming regression detection into a proactive and collaborative process.",
  "summary": null,
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}