{
  "id": 778054,
  "title": "Code review is a taste problem",
  "url": "https://urgent.news/2026/08/13/code-review-is-a-taste-problem",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-08-13T15:00:00.000Z",
  "source": {
    "name": "The New Stack",
    "slug": "the-new-stack",
    "url": "https://thenewstack.io/code-review-taste-problem/"
  },
  "original_language": "en",
  "account": "Code review has evolved into a crucial decision-making process in software engineering, surpassing its original purpose of catching bugs. The true value of code review lies in taste, judgment, and applying organizational processes to assess if a piece of code aligns with the product's goals. As artificial intelligence (AI) generates vast amounts of code, engineering teams face a dilemma: skip code review and risk releasing subpar software, or continue reviewing every line and become overwhelmed with the workload. This has led to heated debates about how to review AI-generated code, as everyone focuses on different aspects of the review process.\n\nAt its core, code review serves three essential functions: collaboration, alignment and knowledge sharing, and verification. By stripping away the line-by-line inspection, code review continues to facilitate collaboration, where teams decide what belongs in the product. It also fosters alignment and knowledge sharing by building shared context about changes in the codebase and the underlying business needs. Lastly, code review verifies the correctness of the code, ensuring it will function as intended and assessing potential risks.\n\nAs AI-generated code becomes more prevalent, the importance of code review is only set to increase. The need for human judgment and expertise becomes even more critical when the volume of code requiring review outpaces the available reviewers. Planning and review processes are merging, with teams now collaborating on code review itself, shaping the product until they collectively decide on the final version.\n\nThe future of code review may move left, with teams capturing intent and decision-making processes before any code is written. This shift places more pressure on code review, but it allows reviewers to focus on the most critical aspects, such as the problem being solved and the constraints in place. This approach optimizes the use of senior engineers' time and preserves the knowledge-sharing function of review.\n\nWhile some argue that engineers shouldn't read every line of code, the key question should be whether their attention is directed to the most useful parts, allowing them to apply their judgment effectively. Different organizations will have varying thresholds for what is considered meaningful, depending on factors such as the sensitivity of the project, the potential consequences of errors, and the impact of mistakes.\n\nAI automation can help address the issue of review feedback becoming repetitive. By analyzing past review comments and categorizing them into deterministic, execution-testable, and genuine judgment feedback, teams can extract codifiable rules and create an \"AI slop register.\" This register, in turn, can be used to enforce rules at the time code is built, leveraging AI to maintain institutional memory and ensure consistency. Ultimately, the future of code review is about directing attention where it is needed most, enabling engineering teams to make informed product decisions and maintain the quality of their software.",
  "summary": "Code review is becoming the most important decision-making surface in software engineering, and it’s outgrowing the diff. The assumption was The post Code review is a taste problem appeared first on The New Stack .",
  "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."
}