[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-arise-lifts-ai-bug-fixing-accuracy-on-open-source-repos":10,"sections":40},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":30,"tags":31,"sources":35,"feedback":39,"feedback_at":22,"cost_usd":39,"total_tokens":39},4224,"arise-lifts-ai-bug-fixing-accuracy-on-open-source-repos","ARISE Lifts AI Bug-Fixing Accuracy on Open-Source Repos","A new graph-based toolset called ARISE helped an open-source coding model resolve 22% of real GitHub issues, up nearly 5 points from the unmodified baseline.","An academic toolset is making AI-driven bug repair meaningfully more precise — without requiring a proprietary model.\n\nARISE (Agentic Repository-level Issue Solving Engine) is a framework-agnostic graph layer that maps not just how a codebase is structured, but how data flows through it. Researchers tested it by mounting it on SWE-agent, a well-known repair scaffold, using the open-source Qwen2.5-Coder-32B-Instruct model as the backbone. On SWE-bench Lite — 300 real GitHub issues spread across 11 Python repositories — that combination resolved 22.0% of issues, compared to 17.3% for the unmodified SWE-agent running the same model. The 4.7 percentage-point gain came almost entirely from sharper fault localization: the system's ability to pinpoint the right function before attempting a fix (Function Recall@1) rose from 0.43 to 0.60, a 40% relative gain, and line-level precision (Line R@1) jumped from 0.26 to 0.41, a 58% relative gain.\n\nThe localization gap matters because most repair frameworks already handle patch synthesis reasonably well once they know where to look. ARISE's core primitive — data-flow slicing, a single API call that traces which statements define or consume a given variable — gives an agent the semantic granularity that purely structural graphs lack. The researchers also ran the same toolset on a second host framework, suggesting the gains are not scaffold-specific.\n\nSWE-bench Lite has become the de facto leaderboard for this class of research, and 22% with a fully open-source backbone is a competitive number — though proprietary-model systems routinely score higher. The authors frame ARISE as a drop-in layer for future work, which is a reasonable pitch, but whether it holds up on codebases outside Python remains an open question.","[\"ai\",\"developer-tools\",\"program-repair\",\"benchmarks\"]","2026-07-07T04:00:00.000Z","2026-07-07T20:25:56.623Z","2026-07-07T20:25:59.319Z","published",null,[24],{"id":25,"reviewer":26,"round":27,"reason":28,"status":29},"editor-r1","editor",1,"The article states Function Recall@1 jumped '57%' relative (implicitly, by calling the line-level gain a '58% relative gain' in the same breath as the function-level jump) but the source material explicitly states Function R@1 rose from 0.43 to 0.60, which is a 40% relative gain — the article omits this figure entirely and could mislead readers into applying the 58% figure to both metrics; correct the function-level relative gain to 40% as documented in the source.","resolved","ai",[30,32,33,34],"developer-tools","program-repair","benchmarks",[36],{"name":37,"url":38},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2605.03117",0,{"sections":41},[42,46,51,56,61,66,71,76,81,85,90,94,99,104],{"name":43,"slug":30,"count":44,"latest_published_at":45},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":47,"slug":48,"count":49,"latest_published_at":50},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":52,"slug":53,"count":54,"latest_published_at":55},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":57,"slug":58,"count":59,"latest_published_at":60},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":62,"slug":63,"count":64,"latest_published_at":65},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":75},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":80},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":18},"Dev Tools","dev-tools",59,{"name":86,"slug":87,"count":88,"latest_published_at":89},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":91,"slug":92,"count":88,"latest_published_at":93},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":105,"slug":106,"count":107,"latest_published_at":108},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]