# Refactoring Published articles for Refactoring. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Migrate a Legacy Monolith Incrementally Without a Big-Bang Rewrite DevFeed: [How to Migrate a Legacy Monolith Incrementally Without a Big-Bang Rewrite]() Original publisher: [Read original article]() Author: Hugo Teijiz Published: 2026-09-17T23:27:03Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [Legacy Modernization](), [migration](), [Refactoring](), [Testing](), [Integration testing](), [observability](), [Routing (disambiguation)](), [Transactions](), [TypeScript](), [API](), [service](), [Dependency injection]() Tags: [artificial-intelligence](), [how-to](), [incremental](), [integration-testing](), [legacy](), [legacy-code](), [migrate](), [migration](), [monolith](), [refactoring](), [rewrite](), [routing](), [software-architecture](), [transactions]() ### AI overview A tutorial on migrating a legacy monolith incrementally by moving one business capability at a time. It covers characterization tests, boundaries between legacy and new code, parallel implementations, progressive traffic, observability, rollback paths, data ownership, and removing migrated behavior. ### Source excerpt Large legacy migrations often fail long before the final cutover. The failure usually starts when the migration is framed as a single event. Move the application. Move the database. Move all the users ## Jemalloc 5.4 Released With Portability Improvements, Code Cleanups DevFeed: [Jemalloc 5.4 Released With Portability Improvements, Code Cleanups]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-17T22:37:11Z Content type: release Language: en Sources: [Phoronix]() Topics: [Code](), [Refactoring](), [test-coverage](), [bug](), [cpu](), [gcc](), [configuration]() Tags: [bug](), [code](), [configuration](), [cpu](), [desktop-linux](), [error-handling](), [gcc](), [improvements](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [process](), [profiling](), [refactoring](), [synchronization](), [test-coverage](), [thread](), [ubuntu-benchmarks](), [ubuntu-hardware](), [yield]() ### AI overview Jemalloc 5.4 is released with portability improvements, code refactoring, bug fixes, expanded test coverage, new allocation features, optimizations, and GCC 16 warning fixes. The release adds an operating-system abstraction layer for platform-dependent operations. ### Source excerpt Earlier this year Meta announced a renewed investment into the jemalloc memory allocator that's been popular for server and HPC use among various desktop use-cases too. After stagnating a bit in recent years, Jemalloc has been off to seeing new developments since Meta's announcement and out today is Jemalloc 5.4 as the latest step forward... ## Slicker: A Tool for Moving Things in Python DevFeed: [Slicker: A Tool for Moving Things in Python]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-04-02T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Python](), [Refactoring](), [Tool](), [PyCharm]() Tags: [bug](), [code](), [engineering](), [infrastructure](), [news](), [pycharm](), [python](), [refactoring](), [tool]() ### AI overview This article explains why Khan Academy built Slicker, a tool for moving thousands of Python files, classes, and functions while updating references and imports. It also describes limitations the authors encountered with PyCharm's refactoring tools. ### Source excerpt By Ben Kraft Craig talked last post about our project to reorganize our whole Python codebase. This entails ... Read more ## The Great Python Refactor of 2017 And Also 2018 DevFeed: [The Great Python Refactor of 2017 And Also 2018]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-03-19T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Python](), [Refactoring](), [Code](), [Development](), [Tool]() Tags: [2017](), [blog-post](), [code](), [dev](), [engineering](), [infrastructure](), [news](), [python](), [refactor](), [refactoring]() ### AI overview This first article in a series describes Khan Academy's 2017-2018 Python refactoring effort. It explains how an overloaded codebase structure developed, the changes made to organize files and directories, and the difficulties of the work. ### Source excerpt By Craig Silverstein Our codebase was a mess. One day, we decided to do something about it: namely, ... Read more ## Intel Thermald 2.5.13 Restores Accidentally Deleted Wildcat Lake Support DevFeed: [Intel Thermald 2.5.13 Restores Accidentally Deleted Wildcat Lake Support]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-11T10:01:22Z Content type: news Language: en Sources: [Phoronix]() Topics: [intel](), [Monitoring](), [Hardware](), [Linux](), [Refactoring](), [Arm]() Tags: [arm](), [code](), [desktop-linux](), [hardware](), [intel](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [monitoring](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [refactoring](), [release](), [ubuntu-benchmarks](), [ubuntu-hardware](), [update](), [wildcat-lake]() ### AI overview Intel Thermald 2.5.13 restores support for Intel's Wildcat Lake platform after it was accidentally removed during refactoring in version 2.5.12. The release also follows recent work to extend Thermald toward Qualcomm/ARM hardware. ### Source excerpt Intel's Thermald released on Thursday as the newest update to this daemon for thermal monitoring on Intel hardware that was also recently extended for beginning to support Qualcomm/ARM hardware too. The main change with Thermald 2.5.13 is adding back Intel Wildcat Lake platform support after it was accidentally removed in the prior release... ## Presentation: Accelerating Performance by Incrementally Integrating Rust Into Existing Codebase DevFeed: [Presentation: Accelerating Performance by Incrementally Integrating Rust Into Existing Codebase]() Original publisher: [Read original article]() Author: Lily Mara Published: 2026-09-10T09:34:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [rust refactoring](), [Refactoring](), [Integration testing]() Tags: [architecture](), [cost](), [culture-methods](), [design](), [development](), [engineering](), [infoq](), [infrastructure](), [performance](), [presentation](), [python](), [qcon-san-francisco-2025](), [qcon-software-development-conference](), [refactoring](), [rust](), [rust-refactoring](), [system-programming](), [testing](), [transcripts]() ### AI overview A presentation on incrementally refactoring Python bottlenecks into Rust with PyO3 to improve function-level performance, retain integration testing, and reduce infrastructure costs. ### Source excerpt Lily Mara explains how to avoid high-risk software rewrites through incremental FFI refactoring. She shares how engineering teams can replace Python bottlenecks with Rust via PyO3, demonstrating how to achieve dramatic function-level speedups, seamless integration testing, and meaningful infrastructure cost savings without microservice overhead. By Lily Mara ## Introducing Consort: Test-driven development on a branching database DevFeed: [Introducing Consort: Test-driven development on a branching database]() Original publisher: [Read original article]() Author: Kevin Hartman Published: 2026-09-09T13:41:25Z Content type: article Language: en Sources: [Databricks]() Topics: [Test-driven development](), [Database](), [Integration testing](), [Software Engineering](), [Refactoring](), [Continuous Delivery (CD)](), [Pull Request](), [Reverse Engineering]() Tags: [code](), [continuous-delivery](), [database](), [development](), [integration](), [partners](), [platform](), [postgres](), [pull-request](), [refactoring](), [reverse-engineering](), [software](), [software-development](), [testing]() ### AI overview The article introduces Consort, a workflow for applying test-driven development to a branching database. It argues that copy-on-write branches of a real database can replace mocks, bring integration testing into the inner development loop, and let engineers run destructive tests in isolation. Versioned schema migrations can move with the dependent code, enabling what the article calls Data CD and helping catch production database problems during pull requests and merges. ### Source excerpt For 25 years I built software on the practices I grew up with: Kent Beck's TDD, Martin... ## A Multi-Module Android Template with Kotlin Convention Plugins, Lint Rules, and Design System Tokens DevFeed: [A Multi-Module Android Template with Kotlin Convention Plugins, Lint Rules, and Design System Tokens]() Original publisher: [Read original article]() Author: Shamil Gulmetov Published: 2026-09-09T01:34:18Z Content type: tutorial Language: en Sources: [ProAndroidDev - Medium]() Topics: [Template](), [Android](), [Architecture & Design](), [Kotlin](), [Gradle](), [Jetpack Compose](), [Dependency injection](), [Automation](), [Refactoring](), [now-in-android]() Tags: [android](), [android-app-development](), [android-architecture](), [android-studio](), [architecture](), [automation](), [best-practices](), [build](), [compose](), [dependency-injection](), [gradle](), [how-to](), [jetpack-compose](), [kotlin](), [now-in-android](), [refactoring]() ### AI overview This tutorial presents an Android Architecture Template based on patterns from Google's Now in Android repository. It describes a multi-module structure with Gradle Version Catalogs, Kotlin Convention Plugins, Jetpack Compose design tokens and dark-theme support, dependency injection, custom Lint rules, and a terminal-based renaming tool. ### Source excerpt How to streamline your development workflow with pre-configured Kotlin Convention Plugins, custom Lint rules, and unified design system tokens. Creating a new Android application from scratch in modern engineering is rarely as simple as clicking File -> New -> New Project in Android Studio. Before writing a single line of business logic, developers face hours -- or even days -- of repetitive infrastructure setup: Configuring a scalable multi-module architecture. Setting up Gradle Version Catalogs and writing custom Kotlin Convention Plugins to eliminate build script duplication. Standardizing design tokens and dark theme support in Jetpack Compose. Wiring up Hilt Dependency Injection for Coroutine Dispatchers, network, and Application Scopes. Enforcing architectural boundaries and team coding standards using custom static analysis (Lint) rules. Google's official Now in Android (NiA) repository is the recognized gold standard for modern Android engineering. However, because NiA is a full-featured showcase app with domain logic (news feeds, authors, bookmarks, offline sync), using it as a starter kit is cumbersome: developers must strip out existing features while risking breaking build pipelines. This Android Architecture Template bridges that gap: a clean, production-ready starter kit that extracts NiA's best architectural patterns into an isolated foundation. To make it enterprise-ready with zero room for error, it includes a standalone automation tool. Instead of manual refactoring, run a single terminal command to rename and brand the entire architecture in seconds. Acknowledgments & Credits Core architectural decisions, build-logic convention plugins, and static analysis infrastructure in this template are directly inspired by Google's official Now in Android (NiA) repository. Rather than reinventing the wheel, this template focuses on developer ergonomics--making Google's best practices instantly usable out of the box. 1. Modular Project Architecture The repository ## SymfonyCon Warsaw 2026: Unveiling the workshop lineup! DevFeed: [SymfonyCon Warsaw 2026: Unveiling the workshop lineup!]() Original publisher: [Read original article]() Author: Eloïse Charrier Published: 2026-09-07T15:35:00Z Content type: news Language: en Sources: [Symfony Blog]() Topics: [Deployment](), [Kubernetes](), [Architecture & Design](), [Refactoring](), [dashboards](), [Vulnerabilities](), [Web Development]() Tags: [agentic](), [ai](), [architecture](), [community](), [deep-dive](), [deployment](), [developer](), [javascript](), [kubernetes](), [news](), [observability](), [orm](), [php](), [production](), [refactoring](), [security](), [symfony](), [vulnerabilities](), [workflows](), [workshops]() ### AI overview SymfonyCon Warsaw 2026 announces its workshop lineup, covering Symfony 8, Kubernetes deployment, clean-architecture refactoring, Doctrine ORM, Symfony AI, Symfony UX, threat modeling, and production observability. Workshops take place on November 24 and 25 in Warsaw, ahead of the main conference. ### Source excerpt We are counting down to SymfonyCon Warsaw 2026, taking place on November 26-27, 2026, in Warsaw (Poland)! Get ready for a week full of workshops, sessions, and community magic. Great news today: all workshops are now online and open for registration!... ## Refactoring Should NOT Touch Your Acceptance Tests DevFeed: [Refactoring Should NOT Touch Your Acceptance Tests]() Original publisher: [Read original article]() Author: Valentina Jemuović Published: 2026-09-03T06:00:37Z Content type: opinion Language: en Sources: [Optivem Journal]() Topics: [Refactoring](), [Pull Request](), [test]() Tags: [change](), [dsl](), [refactor](), [refactoring](), [test](), [tests]() ### AI overview The article argues that a refactoring pull request should not change acceptance tests or their DSL, because doing so changes behavior rather than merely restructuring code. ### Source excerpt A "refactor" PR that changes the acceptance test or the DSL isn't a refactor -- it's a behavioral change ## Becoming an AI Team DevFeed: [Becoming an AI Team]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-09-01T15:01:06Z Content type: opinion Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [AI Strategy](), [AI Development](), [code productivity](), [Refactoring]() Tags: [ai](), [developer-productivity](), [engineering](), [engineering-culture](), [infrastructure](), [pinterest](), [refactoring]() ### AI overview The article argues that becoming an AI team requires changing team ownership, planning, operating models, and roles--not merely adding AI tools. It describes AI-assisted automation, analysis, and prototyping as ways to improve infrastructure reliability, cost efficiency, and developer productivity. ### Source excerpt John Grass | Sr. Manager, Engineering A Fundamental Transformation An AI team is fundamentally more than just a group whose members incorporate AI tools into their existing workflows. The journey to becoming an AI team necessitates a fundamental and comprehensive paradigm shift in how the team defines ownership, engages in strategic planning, and, most critically, executes on its core goals and objectives. This transformation is not merely an addition of new technology; it is a restructuring of the team's operating model, philosophy, and individual roles. Becoming an AI team requires a holistic shift: team members transition from routine, manual execution to empowered, AI-augmented strategists and problem-solvers. In an AI-centric environment, every single member is significantly empowered, not only through access to cutting-edge AI tools and sophisticated models but through an expanded scope of responsibility and influence. These new capabilities allow individuals to automate routine tasks, accelerate data analysis, and rapidly prototype solutions, freeing up cognitive resources for higher-level, more strategic thinking. The expectation shifts from simply completing tasks to orchestrating intelligent systems and focusing on solving problems that were previously intractable. Consequently, the very nature of the work for both individual contributors and managers will be fundamentally different from anything that has come before. At Pinterest, this isn't theoretical. Our infrastructure teams sit at the core of a product that serves billions of Pins, boards, ads, and real-time signals. That reality has forced us to treat "becoming an AI team" as an operational necessity, not a side project as we cannot keep scaling reliability, cost efficiency, and developer productivity using only traditional playbooks. AI Team Capabilities The advent of accessible Artificial Intelligence (AI) tools has fundamentally raised the performance ceiling for what development teams can accomp ## Secretspec 0.20 DevFeed: [Secretspec 0.20]() Original publisher: [Read original article]() Author: domenkozar Published: 2026-08-31T19:28:39Z Content type: release Language: en Sources: [Announcements - NixOS Discourse]() Topics: [Nix](), [Git](), [Docker]() Tags: [announcements](), [dependencies](), [docker](), [git](), [refactoring](), [security](), [split]() ### AI overview SecretSpec 0.20 was released with five new providers, Git and Docker credential helpers, planned resolver IPC for Nix without introducing new dependencies, and separated secrets-handling refactoring in Nix. ### Source excerpt We've released secretspec 0.20, which includes 5 new providers, git and Docker credential helpers. Most importantly we're working towards releasing resolver IPC that Nix will be able to use without introducing new dependencies, which was the main review objection to existing PR. We've also split out secrets handling refactoring in Nix (PR). Disclaimer: LLMs have been used 5 posts - 2 participants Read full topic ## Automating Code Modernization with OpenRewrite - By Stefano Dalla Palma, Adyen DevFeed: [Automating Code Modernization with OpenRewrite - By Stefano Dalla Palma, Adyen]() Original publisher: [Read original article]() Author: Adyen Published: 2026-08-22T06:45:48Z Content type: article Language: en Sources: [Adyen Tech]() Topics: [Refactoring](), [Jackson, Java Dates, Exception, LocalDate](), [migration](), [Tooling](), [Development](), [Software Engineering]() Tags: [code-review](), [internal-developer-platform](), [jackson](), [migration](), [modernization](), [refactoring](), [software](), [software-engineering](), [tech](), [tooling]() ### AI overview An Adyen engineering team explores using OpenRewrite to automate code modernization and reduce the manual effort of framework, API, and static-analysis updates. The article describes a completed migration of an internal Jackson 2 wrapper to Jackson 3 across hundreds of modules and discusses integrating the tool into the build and CI workflow. ### Source excerpt Automating Code Modernization with OpenRewriteBy Stefano Dalla Palma - Development Tooling Engineer, Adyen In a large-scale engineering organization such as Adyen, where hundreds of developers produce hundreds of merge requests per day across thousands of modules, continuously evolving code to meet the latest standards is essential for long-term platform health and performance. However, executing routine updates manually, such as adopting new framework patterns, keeping up with API enhancements, or fine-tuning static analysis rules can introduce friction that takes developers away from building core features. When I joined Adyen's internal developer platform team, I wanted to understand whether any of that friction could be automated away. Around that time, I came across an episode of Software Engineering Radio (link) where the creator of OpenRewrite made an argument that stuck with me: "We are asking framework authors to start taking responsibility for providing recipes when they make breaking changes." I loved that framing. The hassle of keeping up shouldn't fall entirely on the consumers of a library, but on the people who changed the contract in the first place. While we couldn't change how external maintainers worked, we could adopt that mindset internally. I saw an opportunity: if a tool could absorb the friction of a breaking change, we could handle the cleanup on behalf of our developers. But my enthusiasm wasn't going to convince anyone. The engineers I pitched to wanted to see it work before they'd trust it, and I don't blame them. The only way to earn trust was to do the work: pick a real use case, drive it to completion, and let the results speak. One team had a ticket to migrate an internal Jackson 2 wrapper to Jackson 3, a tricky refactoring with deep caller dependencies across hundreds of modules, exactly the kind of high-effort, lower-urgency work that teams understandably prioritize behind customer-facing delivery. I asked if I could take a shot at ## Code Smell 321 - Getter Piggybacking DevFeed: [Code Smell 321 - Getter Piggybacking]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-08-20T02:51:49Z Content type: article Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [Refactoring](), [Code]() Tags: [business-logic](), [class](), [code](), [refactoring]() ### AI overview This article explains a code smell in which an existing getter is reused to implement business logic outside the object that owns the value. It describes the resulting duplicated rules, broken encapsulation, scattered comparisons, and fragile refactoring, and recommends adding behavior methods, keeping comparisons inside the object, passing collaborators instead of primitives, and using getters mainly for rendering. ### Source excerpt TL;DR: Don't reuse an existing getter to bolt on new business logic from outside the object. Problems 😔 Duplicated business rules Broken encapsulation Scattered comparison logic Hidden domain kn ## Refactoring a SQL Table at Scale: Lessons from Harness CI DevFeed: [Refactoring a SQL Table at Scale: Lessons from Harness CI]() Original publisher: [Read original article]() Author: Moshe Tsur Published: 2026-08-06T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Refactoring](), [Structured-data](), [Databases](), [SQL](), [Latency](), [API](), [ci]() Tags: [api](), [continuous-integration](), [harness](), [latency](), [refactoring](), [schema](), [sql]() ### AI overview Harness describes refactoring a flat SQL table used for CI test results into a normalized schema. The article reports reducing storage per row from 400 bytes to 28 bytes and making API latency constant at any scale. ### Source excerpt How Harness refactored a flat SQL table into a normalized schema, cutting storage per row from 400 bytes to 28 bytes and making API latency constant at any scal | Blog ## The Economic Benefit of Refactoring DevFeed: [The Economic Benefit of Refactoring]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-07-30T13:04:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [Refactoring](), [Rust](), [AI Development](), [TypeScript](), [Terraform](), [Terminal]() Tags: [agentic](), [cursor](), [engineering](), [refactoring](), [rust](), [terminal](), [terraform](), [tokens](), [typescript]() ### AI overview An experiment examines whether refactoring an agent-written codebase can reduce the token cost of implementing future features. The application contains about 150,000 lines of primarily Rust code, with TypeScript and Terraform, and includes a 17,155-line data access module that became a target for systematic refactoring. Fresh sub-agents repeat the same representative change after each refactoring step so token consumption can be compared without learning effects. ### Source excerpt Giles Edwards-Alexander does an experiment to see if decomposing a large function helps reduce token costs, suggesting that is may now be possible to measure the economic benefit of refactoring more... ## Three Gradle Talks from KotlinConf 2026 (and What to Steal from Each) DevFeed: [Three Gradle Talks from KotlinConf 2026 (and What to Steal from Each)]() Original publisher: [Read original article]() Author: Laura Kassovic Published: 2026-07-28T04:00:00Z Content type: article Language: en Sources: [The Gradle Blog]() Topics: [Gradle](), [Kotlin](), [Groovy](), [ide](), [Scripting](), [Refactoring]() Tags: [2026](), [best-practices](), [build](), [build-tool](), [conference](), [developer](), [dsl](), [event](), [gradle](), [groovy](), [ide](), [kotlin](), [kotlinconf](), [refactoring](), [script](), [scripting](), [talk](), [talks]() ### AI overview The article reviews three Gradle talks from KotlinConf 2026. It explains the progression from Groovy build scripts to the Kotlin DSL and early Declarative Gradle, emphasizing the problems each approach addresses. It also introduces Gradle best practices for organizing and optimizing builds. ### Source excerpt Every year I tell myself I'll watch every conference talk I missed. Every year I get through maybe three before a build breaks and I get pulled back into real life. KotlinConf is the one event where I actually follow through, partly because the talks are short and dense, and partly because a good chunk of my coworkers and friends are on stage. This year in Munich, three of them talked about Gradle: Paul Merlin on the history of our DSLs, Stefan Wolf on best practices, and Marcin Mycek on Declarative Gradle for Kotlin. I watched all three so you can decide which ones are worth your fifteen minutes. A Tale of the Gradle DSLs -- Paul Merlin Paul has been inside Gradle's DSL machinery for years, which makes him exactly the right person to tell this story and exactly the wrong person to pretend the early days were clean. The talk traces how build logic went from Groovy, to the Kotlin DSL, to Declarative Gradle. The useful part isn't the timeline. It's that each of those was solving a real problem the previous one couldn't. Groovy gave us a flexible, dynamic scripting language, which was great right up until you wanted your IDE to tell you what was valid. The Kotlin DSL brought static typing, real autocompletion, and refactoring that doesn't rely on hope. That's why the Kotlin DSL is now the default for new Gradle builds. Declarative Gradle is the next swing: describe what your build is rather than script how it runs. The honest through-line is that none of these fully replaces the last one. Kotlin DSL is where most people should be today. Groovy still runs an enormous amount of the world's build logic and isn't going anywhere fast. Declarative Gradle is early but points at where things are headed. Real talk: if your build scripts are still Groovy and still working, you don't have to panic-migrate this weekend. But if you're starting something new, reach for the Kotlin DSL and let the IDE carry some of the weight. 📺 Watch the talk 10 Gradle Best Practices Every Kotlin Deve ## Porting Django to Go with AI Without the Naive Rewrite DevFeed: [Porting Django to Go with AI Without the Naive Rewrite]() Original publisher: [Read original article]() Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Django](), [Go Language](), [Artificial Intelligence](), [migration](), [Testing](), [test-coverage](), [Refactoring](), [proxy]() Tags: [ai](), [claude](), [copilot](), [django](), [go](), [migration](), [proxy](), [refactoring](), [test-coverage](), [testing]() ### AI overview The article argues that porting a Django API to Go with AI requires strong engineering foundations rather than line-by-line translation. It recommends tests, production-shaped traffic, clear module boundaries, code review, and incremental rollout mechanisms to establish behavioral parity and reduce migration risk. ### Source excerpt . [Porting Django to Go with AI Without the Naive Rewrite](porting-django-to-go-with-ai-cover... ## Prefactoring: Clear the Way for Your New Feature DevFeed: [Prefactoring: Clear the Way for Your New Feature]() Original publisher: [Read original article]() Author: Google Testing Bloggers (noreply@blogger.com) Published: 2026-07-21T18:52:05Z Content type: tutorial Language: en Sources: [Google Testing Blog]() Topics: [Refactoring](), [Code]() Tags: [article](), [code](), [rahul-singal](), [refactoring](), [tott]() ### AI overview The article explains prefactoring, or preparatory refactoring: restructuring existing code before implementing a planned feature. It presents this as a way to make feature work fit the code naturally, speed up reviews, reduce bugs, and enable safer rollbacks. ### Source excerpt This article was adapted from a Google Tech on the Toilet (TotT) episode. You can download a printer-friendly version of this TotT episode and post it in your office. By Rahul Singal "First make the change easy, then make the easy change." - paraphrased from Kent Beck You're working on a new feature, but the existing code wasn't written with future changes in mind. Trying to force the feature in directly gets complicated fast. One change leads to another, and before you know it you're already a few files deep fixing things you never planned to touch. Prefactoring (short for "preparatory refactoring") is the practice of reworking existing code to make it more suitable for an upcoming change before you actually implement the new functionality. Instead of cleaning up code as an afterthought or trying to force a new feature into an incompatible structure, you restructure the codebase first. Prefactoring helps you: Easily implement new features: Restructuring the codebase first ensures your new feature fits naturally into the code. Speed up reviews: It's easier to review the refactoring and the feature in separate changes. Avoid bugs: Isolating cleanups from functional logic can help prevent bugs. Roll back safely: If you need to roll back, it is much easier to revert small, focused changes. Here is a simplified example of a prefactoring change: Change 1 (Prefactoring) Extract display name helper to remove duplication. Change 2 (Feature) Add middle name support. + def get_display_name(user): + return f"{user.first_name} {user.last_name}" # Profile page - display_name = f"{user.first_name} {user.last_name}" + display_name = get_display_name(user) # Email template - greeting = f"Hi {user.first_name} {user.last_name}," + greeting = f"Hi {get_display_name(user)}," def get_display_name(user): - return f"{user.first_name} {user.last_name}" + return f"{user.first_name} {user.middle_name} {user.last_name}" You can prefactor a change that is already in review too! If your reviewe ## How to clean up AI-generated code with Fallow DevFeed: [How to clean up AI-generated code with Fallow]() Original publisher: [Read original article]() Author: David Omotayo Published: 2026-07-21T13:00:28Z Content type: tutorial Language: en Sources: [LogRocket Blog]() Topics: [Code quality](), [AI-assisted coding](), [TypeScript](), [JavaScript](), [Refactoring]() Tags: [ai](), [ai-coding](), [analysis](), [article](), [blog](), [code](), [code-quality](), [coding](), [complexity](), [dev](), [developer](), [development](), [how-to](), [javascript](), [learn](), [refactoring](), [software](), [tool](), [typescript](), [uncategorized](), [workflow]() ### AI overview This tutorial explains how to use Fallow, a TypeScript and JavaScript code analysis tool, to identify unused code, duplicate logic, and complexity in AI-generated code. It also covers setting up Fallow, interpreting its reports, and integrating automated code-quality checks into an AI-assisted development workflow. ### Source excerpt Learn how to use Fallow to analyze AI-generated code, detect dead code, duplicate logic, and complexity issues, and integrate automated code quality checks into your AI-assisted development workflow. The post How to clean up AI-generated code with Fallow appeared first on LogRocket Blog. ## Modernizing a Legacy Java 1.5 Codebase with Evidence-Grounded AI and Docker DevFeed: [Modernizing a Legacy Java 1.5 Codebase with Evidence-Grounded AI and Docker]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-07-16T13:25:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [Legacy Modernization](), [Java](), [Large Language Model](), [Refactoring](), [Docker]() Tags: [docker](), [java](), [legacy-modernization](), [llms](), [refactoring](), [tests]() ### AI overview The article describes modernizing a Java 1.5 codebase for current hardware. It finds that LLM output was unreliable when used without repository evidence, while progress came from evidence-based analysis, Docker validation, and gradual refactoring protected by tests. ### Source excerpt When people think of legacy modernization, most folks aren't imagining the target environment will be Java 8. But this was the challenge facing Nik Malykhin when he needed to run a Java 1.5 codebase on today's hardware. His early use of LLMs gave plausible answers that did not hold up in the codebase. Progress came when he grounded the process in evidence, using AI to support analysis, validation in a stable Docker environment, and gradual refactoring protected by tests. The main takeaway is practical: AI was most useful when constrained by evidence, clear roles, and a step-by-step modernization strategy. more... ## Live coding with dir stepper DevFeed: [Live coding with dir stepper]() Original publisher: [Read original article]() Published: 2026-07-16T00:00:00Z Content type: article Language: en Sources: [Jake Wharton]() Topics: [intellij-platform](), [IntelliJ IDEA](), [Git](), [ide](), [Code](), [Documentation]() Tags: [code](), [coding](), [git](), [ide](), [intellij-idea](), [intellij-platform](), [kotlinconf](), [plugin](), [presentation](), [procedural](), [refactoring]() ### AI overview The article describes creating a workflow for live-coding presentations after finding that Git commits and interactive rebases were cumbersome for managing sequential coding steps. It explores using IntelliJ IDEA as an extensible platform for a tool that can move through steps, jump to completed states, refactor steps, and keep the process unobtrusive for the presenter and audience. ### Source excerpt Two months ago I gave my first ever live programming talk at KotlinConf. It was called "Talking to terminals (and how they talk back)", and you can watch it here. I've previously done slide-heavy talks which contained small demos or navigating through an existing codebase. Since this talk was starting from nothing, I decided to just write all the code from nothing. This meant spending the majority of the time in the IDE, but also figuring out how to remember what I was supposed to be writing at each step. Making the wrong choice The exceedingly obvious solution to having a series of steps in a live coding presentation is a git repo and a commit for each step. Unfortunately, this is a huge pain in the ass and really doesn't work at all (at least not for me). I started with commits, but by the 7th or 8th commit I was spending more than half my time doing interactive rebases over the entire history. Need a function in step 8 that should've been extracted between step 3 and 4? That's another 10 minutes rewriting history. Because it's a small set of files that are changing, every commit conflicts with any changes to history. It's also not clear how you actually move through the history. We want to start from nothing and move forward through history. Generally, git helps you jump backwards from the latest, not stepping forward from the oldest. It's not impossible, it's just clearly going against the grain. The problem, defined Now thoroughly enjoying this distraction from writing the actual talk, I distilled what I was trying to solve. Ability to manually write the changes required or to simply jump to the finished step. Some steps are important to watch unfold while others are procedural. This also can help with time management when I (inevitably) run low on time. Easily refactor steps as I go. Later steps often requiring refactoring previous ones to minimize the diff. As the talk evolves I want to add or merge steps. Nearly invisible to me and the viewer. I don't want t ## Building a Testable Network Layer and Three-Tier State Management in Flutter DevFeed: [Building a Testable Network Layer and Three-Tier State Management in Flutter]() Original publisher: [Read original article]() Author: Veli Bacık Published: 2026-07-08T15:56:52Z Content type: tutorial Language: en Sources: [Flutter Community - Medium]() Topics: [Flutter](), [Network](), [API](), [HTTP](), [Refactoring](), [Error Handling](), [JSON](), [ui](), [App]() Tags: [api](), [backend](), [building](), [code](), [dart](), [error-handling](), [flutter](), [how-to](), [http](), [implementation](), [integration](), [json](), [masterclass](), [network](), [refactoring](), [server](), [state-management]() ### AI overview This tutorial explains how to refactor a Flutter application's network and state-management architecture. It introduces a generic, testable network manager and service/model layers, then applies a three-tier state model using Cubit for business state, BlocSelector for targeted rebuilds, and ValueNotifier for widget-local UI state. ### Source excerpt How to wrap HTTP behind a generic, testable network manager with sealed results -- then drive your screens with a three-tier state model: global Cubit, page Cubit, and widget-local ValueNotifier. Flutter Refactoring Masterclass -- Part 3 GitHub - VB10/flight_booking: Flight App for refactoring real project PRs: #11, #12 📺 Video Series: Network & Service Layer https://medium.com/media/2d40d555e4d8968f1d15fb35e2e91553/hrefhttps://medium.com/media/3d8a8a32a77c98177ba10492a892aed6/href 🤖 Want to apply these changes to your project? See the AI prompt at the end. The Problem In Part 2 we split the login page into View, ViewModel, and Mixin -- but the ViewModel still new'd up a Dio() instance and hard-coded http://localhost:8080 inline. Every screen that talked to the backend did the same thing: create a fresh Dio, paste the base URL, decode JSON by hand, and copy-paste the same if (statusCode == 200) ... else ... catch (e) ladder. That pattern rots fast. A URL change means a project-wide search. A new header (auth token, API version) has to be added everywhere. Error handling drifts -- one screen returns 'Server hatası', another swallows the exception. And nothing is testable, because the network call is welded to the widget. As the video puts it: the person writing the code shouldn't be able to say "this only works with internet" -- the network is a dependency, and dependencies must be managed. There's a second rot: state. setState rebuilds the whole widget on every field change. A page with a loading flag, an error string, and a list re-renders all three even when only one changed. And there's no consistent home for "where does the loading state live" -- sometimes a ValueNotifier, sometimes a bool, sometimes buried in _PageState. This article fixes both on the same module. PR #11 builds a generic network + service + model layer. PR #12 layers a three-tier state model on top -- Cubit for business, BlocSelector for surgical rebuilds, ValueNotifier for widget-local UI. What We Change ## How we migrated a live routing system using AI-assisted refactoring DevFeed: [How we migrated a live routing system using AI-assisted refactoring]() Original publisher: [Read original article]() Author: Arnold Wakim Published: 2026-06-23T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI-assisted coding](), [migration](), [Test-driven development](), [cursor](), [Kafka]() Tags: [ai](), [claude](), [cursor](), [duckdb](), [kafka](), [migration](), [postgresql](), [production](), [refactoring](), [routing](), [storage]() ### AI overview An engineering account of migrating Stream Router's storage architecture with AI-assisted, test-driven refactoring while preserving live production traffic. ### Source excerpt Using AI-assisted refactoring, we migrated our live routing brain to a relational model, safely validating changes against live production traffic. [Next page]()