--- title: A Testing Strategy for Legacy Modernisation | Insights description: Legacy modernisation projects fail at a high rate partly because legacy systems lack a written specification, making conventional testing impossible.... canonical: https://daily.dev/posts/a-testing-strategy-for-legacy-modernisation-insights-bckoasyqj twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: A Testing Strategy for Legacy Modernisation | Insights | daily.dev og:description: Legacy modernisation projects fail at a high rate partly because legacy systems lack a written specification, making conventional testing impossible.... og:url: https://daily.dev/posts/a-testing-strategy-for-legacy-modernisation-insights-bckoasyqj og:image: https://api.daily.dev/og/posts/bckOASYqJ.png og:image:alt: A Testing Strategy for Legacy Modernisation | Insights og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # A Testing Strategy for Legacy Modernisation | Insights **[Dev Genius](https://daily.dev/sources/devgenius)** · 9 min read · 0 upvotes · 0 comments ## Summary Legacy modernisation projects fail at a high rate partly because legacy systems lack a written specification, making conventional testing impossible. Characterisation testing, a technique from Michael Feathers, solves this by capturing existing system behaviour as a baseline before changes are made, rather than asserting what behaviour should be. A full strategy layers characterisation tests, parallel/shadow runs, risk-prioritised regression coverage, and a deliberate process for classifying behavioural differences as bug fixes or regressions. AI tooling can help generate characterisation tests at scale and analyse legacy codebases, but judgement about which behaviours to preserve remains a human responsibility. Success is measured via parity rate against the characterisation baseline and post-migration defect escape rate, and this work should start at discovery rather than being compressed into the end of a project. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is a characterisation test and how does it differ from a regular unit test? A characterisation test documents the actual behavior a piece of code currently produces, rather than asserting what that behavior should be. Instead of writing an assertion like 'this function should return X,' it observes what the function currently returns for a given input and records that as the baseline, regardless of whether the result looks correct. This concept originates from Michael Feathers' work on legacy code. _daily.dev surfaces practical testing techniques like this for engineers modernizing legacy systems._ ### How should teams measure whether a legacy modernisation testing strategy is working? Two metrics matter most: parity rate against the characterisation baseline, which tracks how often the new system's output matches the legacy system's recorded behavior versus deliberately diverges from it, and defect escape rate post-migration, which measures discrepancies caught before go-live versus discovered afterward in production. Both give a clearer signal than raw test count or coverage percentage, which can look healthy while still missing undocumented edge cases. _Track metrics like parity rate and defect escape rate by following legacy modernisation practices on daily.dev._ ### What percentage of enterprises use AI for regression suite and script maintenance in testing? 65.5% of large enterprises report using AI specifically for regression suite and script maintenance work, according to the 2026 State of Testing research, a meaningfully higher figure than smaller organizations report. This aligns with where AI tooling is strongest: sustaining and extending test coverage on systems that already have some automated baseline in place. _Developers weighing AI adoption for test maintenance can track adoption trends like this on daily.dev._ ## Similar posts on daily.dev - [How to Build Characterization Tests Before Refactoring Legacy Code](https://daily.dev/posts/how-to-build-characterization-tests-before-refactoring-legacy-code-5jwk11sf3) · freeCodeCamp · 1 upvotes · 1 comments - [Empirical Characterization Testing](https://daily.dev/posts/empirical-characterization-testing-ujnz2h7xn) · ploeh blog · 0 upvotes · 0 comments - [How to Use Differential Testing During a Legacy Migration](https://daily.dev/posts/how-to-use-differential-testing-during-a-legacy-migration-epakjbafd) · freeCodeCamp · 0 upvotes · 0 comments - [Test Strategy for a Product That Outgrows Its Own Process](https://daily.dev/posts/test-strategy-for-a-product-that-outgrows-its-own-process-kje2eerps) · Software Testing Magazine · 0 upvotes · 0 comments - [Lessons in Testing, Performance, and Legacy Systems from](https://daily.dev/posts/lessons-in-testing-performance-and-legacy-systems-from-qd1hkgdfb) · GitGuardian · 0 upvotes · 0 comments --- Tags: [#testing](https://daily.dev/tags/testing) [View this post on daily.dev](https://daily.dev/posts/a-testing-strategy-for-legacy-modernisation-insights-bckoasyqj) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"A Testing Strategy for Legacy Modernisation | Insights","url":"https://daily.dev/posts/a-testing-strategy-for-legacy-modernisation-insights-bckoasyqj","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/a-testing-strategy-for-legacy-modernisation-insights-bckoasyqj"},"datePublished":"2026-08-16T20:00:48.295Z","dateModified":"2026-09-14T07:17:16.350Z","description":"Legacy modernisation projects fail at a high rate partly because legacy systems lack a written specification, making conventional testing impossible....","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/6a0478924c3f2ae375f33ec0c4baa267?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/6a0478924c3f2ae375f33ec0c4baa267?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Dev Genius","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Dev Genius","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/a79144d7517043e7803ed562e45a99df","url":"https://daily.dev/sources/devgenius"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/a-testing-strategy-for-legacy-modernisation-insights-bckoasyqj","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"testing","timeRequired":"PT9M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Dev Genius","item":"https://daily.dev/sources/devgenius"},{"@type":"ListItem","position":3,"name":"A Testing Strategy for Legacy Modernisation | Insights"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/a-testing-strategy-for-legacy-modernisation-insights-bckoasyqj#faq","mainEntity":[{"@type":"Question","name":"What is a characterisation test and how does it differ from a regular unit test?","acceptedAnswer":{"@type":"Answer","text":"A characterisation test documents the actual behavior a piece of code currently produces, rather than asserting what that behavior should be. Instead of writing an assertion like 'this function should return X,' it observes what the function currently returns for a given input and records that as the baseline, regardless of whether the result looks correct. This concept originates from Michael Feathers' work on legacy code. daily.dev surfaces practical testing techniques like this for engineers modernizing legacy systems."}},{"@type":"Question","name":"How should teams measure whether a legacy modernisation testing strategy is working?","acceptedAnswer":{"@type":"Answer","text":"Two metrics matter most: parity rate against the characterisation baseline, which tracks how often the new system's output matches the legacy system's recorded behavior versus deliberately diverges from it, and defect escape rate post-migration, which measures discrepancies caught before go-live versus discovered afterward in production. Both give a clearer signal than raw test count or coverage percentage, which can look healthy while still missing undocumented edge cases. Track metrics like parity rate and defect escape rate by following legacy modernisation practices on daily.dev."}},{"@type":"Question","name":"What percentage of enterprises use AI for regression suite and script maintenance in testing?","acceptedAnswer":{"@type":"Answer","text":"65.5% of large enterprises report using AI specifically for regression suite and script maintenance work, according to the 2026 State of Testing research, a meaningfully higher figure than smaller organizations report. This aligns with where AI tooling is strongest: sustaining and extending test coverage on systems that already have some automated baseline in place. Developers weighing AI adoption for test maintenance can track adoption trends like this on daily.dev."}}]} ```