WhatIs.com 09月29日
AI编程工具影响软件交付稳定性
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谷歌DORA调查发现,AI编程工具不再延缓软件交付速度,但仍然影响稳定性并加剧组织问题。调查涵盖交付速度、效率和可靠性,以及开发者代码质量、摩擦和倦怠。结果显示,每组织AI采用率每增加25%,软件交付吞吐量下降1.5%,交付稳定性下降7.2%。报告提出AI可能通过处理基础编码工作(如脚手架、模板、常规转换)使开发者更专注于部署,从而提高吞吐量。此外,开发者对AI生成的输出信任度增加,但未达预期,反映出对AI能力的合理预期调整。

谷歌DORA调查发现,AI编程工具使用与软件交付稳定性下降相关,但不再显著影响交付速度。每组织AI采用率每增加25%,软件交付吞吐量下降1.5%,交付稳定性下降7.2%,表明AI在提高效率的同时仍存在稳定性问题。

AI可能通过自动化基础编码工作(如脚手架、模板、常规转换)提升开发效率,使开发者更专注于复杂任务,从而提高交付速度。报告认为AI处理底层工作使开发者有更多时间部署代码,进而改善产品性能。

尽管AI采用率从2024年的76%增至2025年的90%,但开发者对AI的信任度并未同步提升。30%的受访者表示对AI“有点”或“完全不信任”,而70%表示“ somewhat ”或“ a lot ”信任AI输出,反映出对AI能力的合理预期调整。

DORA报告提出七项AI最佳实践:明确AI立场、健康数据生态、内部数据可访问性、强版本控制、小批量工作、用户中心焦点、高质量内部平台,并指出AI效果取决于组织如何应用这些实践而非是否使用AI。

分析师指出,IT决策者虽愿意为具备高质量AI功能的软件支付更多费用并声称信任AI决策,但在实践中常覆盖AI建议,显示对AI能力的敬畏与对其局限性的挫败感并存。

<p>AI coding tools, now ubiquitous among software developers, no longer delay software delivery but still impact stability and amplify organizational issues, a Google DORA survey found.</p><div class="ad-wrapper ad-embedded"> <div id="halfpage" class="ad ad-hp"> <script>GPT.display('halfpage')</script> </div> <div id="mu-1" class="ad ad-mu"> <script>GPT.display('mu-1')</script> </div> </div> <p>The Google research group's survey, conducted annually, measures software delivery performance in two main categories: speed and efficiency, or throughput, and quality and reliability of releases, termed instability. It also measures individual software developer outcomes such as code quality, friction and burnout.</p> <p>Last year, <a href="https://www.techtarget.com/searchsoftwarequality/news/366614786/GitHub-Copilot-Autofix-expands-as-AI-snags-software-delivery"&gt;DORA's survey</a> of 3,000 respondents found a decrease of 1.5% in software delivery throughput and a 7.2% decrease in delivery stability for every 25% increase in an organization's AI adoption. This year, among 5,000 survey respondents and more than 100 hours of research interviews, those outcomes were measured differently, but significantly differed from last year's results, according to Nathen Harvey, DORA lead and developer advocate at Google Cloud.</p> <p>"We're using standardized effects this year, looking at how much of a change there is as [respondents are] using more AI, and we measure those changes in standard deviations from the mean," Harvey said. "Essentially what we're saying is that these numbers are relative but show improvements. It's not a huge improvement, but it is decidedly an improvement."</p> <p>DORA's State of AI-assisted Software Development <a target="_blank" href="https://blog.google/technology/developers/dora-report-2025/" rel="noopener">report</a> this week poses some hypotheses on what accounted for this change.</p> <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/harvey_nathen.jpg" alt="Nathen Harvey, DORA lead, Google Cloud">Nathen Harvey</div> <p>"If AI is handling some of the grunt work underlying coding processes (scaffolding, boilerplate, routine transformations), developers may have more time to focus on deploying code, leading to increased software delivery throughput and ultimately to improved product performance," the report reads. "We could also be observing organizational systems adapting into more fruitful environments for AI."</p> <p>These results resonated with one software engineering leader.</p> <p>"I see more engineers finding a better collaborative relationship with AI tools," said David Strauss, chief architect and co-founder at WebOps company Pantheon. "Expectations are more reasonable, and models have improved in quality as well."</p> <p>Harvey said the adverse effect of AI on software release stability is to be expected as a technology matures.</p> <p>"It's no surprise, honestly, that we see throughput starting to inch up first before instability goes down," he said. "There's always pressure to move faster, move faster, move faster, and then stability kind of comes second."</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/dora_aIimpact_2025-f.jpg"&gt; <img data-src="https://www.techtarget.com/rms/onlineimages/dora_aIimpact_2025-f_mobile.jpg" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/dora_aIimpact_2025-f_mobile.jpg 960w,https://www.techtarget.com/rms/onlineimages/dora_aIimpact_2025-f.jpg 1280w" alt="Google DORA State of AI-Assisted Software Development 2025" data-credit="Google DORA" height="316" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Google DORA found the use of AI coding tools no longer correlate with software delivery throughput slowdowns, but still pose instability issues. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div></figure> <section class="section main-article-chapter" data-menu-title="AI trust issues rise"> <h2 class="section-title"><i class="icon" data-icon="1"></i>AI trust issues rise</h2> <p>Other changes revealed in this year's survey included an increase in developer adoption of AI, from 76% in 2024 to 90% in 2025. More than 80% of respondents reported increased productivity and 59% reported an increase in code quality.</p> <p>However, while AI adoption increased significantly, respondents' trust in the technology didn't improve proportionately, Harvey said: 30% of respondents said they trust AI "a little" or "not at all," down from 39.2% last year, but this year 70% said they trusted AI-generated outputs "somewhat," "a lot," or "a great deal," versus 87.9% in 2024.</p> <p>Harvey interpreted these results as a healthy adjustment in expectations for what AI coding tools can do.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> I believe people are caught between awe for AI's current capabilities and frustration over its inability to truly understand the world. The probabilistic nature of AI makes it hard for people to fully understand and trust it. </figure> <figcaption> <strong>Torsten Volk</strong>Analyst, Omdia </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>"The reality is you shouldn't trust something 100% — 100% trust in AI would be wrong," he said. "With software delivery instability continuing to increase, we have to make sure that we have checks in place to validate what's coming out."</p> <p>One industry analyst said his own research shows a similar disconnect between the fact that IT decision makers are willing to pay more for software with high-quality AI features and claim to trust AI-based decisions, but also frequently overwrite AI decisions in practice.</p> <p>"I believe people are caught between awe for AI's current capabilities and frustration over its inability to truly understand the world," said Torsten Volk, an analyst at Omdia, a division of Informa TechTarget. "The probabilistic nature of AI makes it hard for people to fully understand and trust it. Sometimes AI provides responses that seem human-like, while at other times, its responses are illogical."</p></section> <section class="section main-article-chapter" data-menu-title="AI best practices emerge"> <h2 class="section-title"><i class="icon" data-icon="1"></i>AI best practices emerge</h2> <p>Overall, the DORA report found that <a href="https://www.techtarget.com/searchenterpriseai/opinion/Can-AI-write-code-A-developer-experiments-in-two-languages"&gt;AI usage</a> has begun to mirror and amplify existing organizational traits, both for better and for worse.</p> <p>"We're seeing mixed results with AI across the people that we're looking at, and at the same time, we're seeing 90% of people using AI," Harvey said. "So it's clear that it's not whether or not you're using AI that's driving this, but rather, how you're using AI that's driving its impact."</p> <p>Based on this year's survey results, DORA identified seven best practices common to organizations benefiting from AI:</p> <ul class="default-list"> <li>A clear and communicated AI stance.</li> <li>A healthy data ecosystem.</li> <li>AI-accessible internal data.</li> <li>Strong version control practices.</li> <li>Working in small batches.</li> <li>User-centric focus.</li> <li>A quality internal platform.</li> </ul> <p>DORA also identified how different applications of these practices can lead to better specific AI outcomes. For example, a team that wants to improve its product performance while using AI should focus on having <a href="https://www.techtarget.com/searchitoperations/feature/Platform-teams-draw-on-DataOps-MLOps-to-support-GenAI"&gt;accessible internal data</a>, working in small batches, and clarifying its AI stance.</p> <p>"What I tell organizations looking to take advantage of AI is that their house better be in order," said Matthew Flug, an analyst at IDC. "Their workflows, processes, and security posture all need to be rock solid, because AI will find the gaps in the armor."</p> <p><i>Beth Pariseau, a senior news writer for Informa TechTarget, is an award-winning veteran of IT journalism covering DevOps. Have a tip? </i><a href="mailto:beth.pariseau@informatechtarget.com?subject=News%20tip"><i>Email her</i></a><i> or reach out </i><a target="_blank" href="https://x.com/PariseauTT" rel="noopener"><i>@PariseauTT</i></a><i>.</i></p></section>

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AI编程工具 软件交付 DORA报告 AI稳定性 开发者信任 AI最佳实践 效率与稳定性 谷歌DORA
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