{"id":996,"date":"2026-02-05T18:45:56","date_gmt":"2026-02-05T17:45:56","guid":{"rendered":"https:\/\/metrics.blogg.gu.se\/?p=996"},"modified":"2026-01-22T19:10:03","modified_gmt":"2026-01-22T18:10:03","slug":"the-close-future-of-software-engineering","status":"publish","type":"post","link":"https:\/\/metrics.blogg.gu.se\/?p=996","title":{"rendered":"The close future of software engineering"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/metrics.blogg.gu.se\/files\/2026\/01\/Embedded-software-engineering-pipeline-comparison.jpg\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/metrics.blogg.gu.se\/files\/2026\/01\/Embedded-software-engineering-pipeline-comparison-1024x683.jpg\" alt=\"\" class=\"wp-image-997\" srcset=\"https:\/\/metrics.blogg.gu.se\/files\/2026\/01\/Embedded-software-engineering-pipeline-comparison-1024x683.jpg 1024w, https:\/\/metrics.blogg.gu.se\/files\/2026\/01\/Embedded-software-engineering-pipeline-comparison-300x200.jpg 300w, https:\/\/metrics.blogg.gu.se\/files\/2026\/01\/Embedded-software-engineering-pipeline-comparison-768x512.jpg 768w, https:\/\/metrics.blogg.gu.se\/files\/2026\/01\/Embedded-software-engineering-pipeline-comparison-1200x800.jpg 1200w, https:\/\/metrics.blogg.gu.se\/files\/2026\/01\/Embedded-software-engineering-pipeline-comparison-1320x880.jpg 1320w, https:\/\/metrics.blogg.gu.se\/files\/2026\/01\/Embedded-software-engineering-pipeline-comparison.jpg 1536w\" sizes=\"(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 1362px) 62vw, 840px\" \/><\/a><\/figure>\n\n\n\n<p><a href=\"https:\/\/www.arxiv.org\/pdf\/2601.10220\">https:\/\/www.arxiv.org\/pdf\/2601.10220<\/a><\/p>\n\n\n\n<p>We\u2019re witnessing a <strong>transformative shift in embedded software engineering<\/strong> as <strong>generative AI moves from a tool to an active participant in development pipelines<\/strong>. In our recent study, we explored how embedded software teams\u2014especially in safety-critical and resource-constrained domains\u2014are adapting to this change. Unlike conventional programming, embedded systems demand <strong>determinism, reliability, and traceability<\/strong>, attributes that stochastic, AI-generated artifacts can undermine.<\/p>\n\n\n\n<p>Through <strong>qualitative interviews and structured brainstorming with senior engineers across four companies<\/strong>, we identified <strong>eleven emerging practices and fourteen challenges<\/strong> shaping generative AI adoption. Central to these practices is the concept of <strong>agentic pipelines<\/strong>\u2014multi-agent continuous integration and delivery flows where generative agents collaborate across coding, compiling, testing, and validation. Key practices include designing <strong>AI-friendly artifacts<\/strong>, integrating <strong>compiler-in-the-loop feedback<\/strong>, and managing prompt repositories for auditability and consistency.<\/p>\n\n\n\n<p>Equally important are <strong>governance and sustainability concerns<\/strong>. Teams emphasize <strong>human-in-the-loop supervision<\/strong>, formal governance frameworks, traceability of models and outputs, and workforce upskilling to responsibly harness AI automation. Our findings reveal that while generative AI offers substantial productivity gains, <strong>sustainable adoption in embedded systems hinges on balancing autonomy with accountability<\/strong>\u2014without compromising safety or certification requirements.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>https:\/\/www.arxiv.org\/pdf\/2601.10220 We\u2019re witnessing a transformative shift in embedded software engineering as generative AI moves from a tool to an active participant in development pipelines. In our recent study, we explored how embedded software teams\u2014especially in safety-critical and resource-constrained domains\u2014are adapting to this change. Unlike conventional programming, embedded systems demand determinism, reliability, and traceability, attributes that &hellip; <a href=\"https:\/\/metrics.blogg.gu.se\/?p=996\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;The close future of software engineering&#8221;<\/span><\/a><\/p>\n","protected":false},"author":68,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"_links":{"self":[{"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/posts\/996"}],"collection":[{"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/users\/68"}],"replies":[{"embeddable":true,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=996"}],"version-history":[{"count":1,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/posts\/996\/revisions"}],"predecessor-version":[{"id":998,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/posts\/996\/revisions\/998"}],"wp:attachment":[{"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=996"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=996"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=996"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}