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ACM Program. Lang."],"published-print":{"date-parts":[[2026,4,10]]},"abstract":"<jats:p>\n                    Large language models (LLMs) for code editing have achieved remarkable progress, yet recent empirical studies reveal a fundamental disconnect between\n                    <jats:italic toggle=\"yes\">technical accuracy<\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">developer productivity<\/jats:italic>\n                    . Despite their strong benchmark performance, developers complete tasks 19% slower when using AI assistance, with over 68.81% of recommendations disrupting their mental flow. This misalignment stems from the use of static commit snapshots that lack temporal information, causing models to optimize for end results rather than the incremental, context-sensitive steps that align with developers\u2019 natural reasoning process.\n                  <\/jats:p>\n                  <jats:p>\n                    To bridge this gap, we present\n                    <jats:bold>EditFlow<\/jats:bold>\n                    , which benchmarks and optimizes subsequent code edit recommendation systems through the reconstruction of developer editing flows. EditFlow addresses three key challenges. First, collecting edit-order data that reflects developers\u2019 flow is inherently difficult: manual annotation introduces prohibitive overhead, while development logs capture only single trajectories instead of all plausible editing flows. Second, benchmarking recommendation performance against developers\u2019 ongoing editing flow requires a\n                    <jats:italic toggle=\"yes\">digital-twin-like simulation<\/jats:italic>\n                    that can faithfully simulate the editing process. Third, existing heterogeneous systems vary drastically in scale and architecture, posing challenges for developing a unified optimization strategy that endows all models with\n                    <jats:italic toggle=\"yes\">mental-flow awareness<\/jats:italic>\n                    regardless of design or capability.\n                  <\/jats:p>\n                  <jats:p>\n                    To overcome these challenges, we propose three tightly coupled components: (1) a\n                    <jats:bold>prompt auto-tuning mechanism<\/jats:bold>\n                    that learns an optimized prompt for inferring the relative order between two edits, (2) a\n                    <jats:bold>digital twin<\/jats:bold>\n                    that replays reconstructed edit sequences to simulate developers\u2019 editing process, and (3)\n                    <jats:bold>EditFlow<\/jats:bold>\n                    , a\n                    <jats:bold>unified optimization strategy<\/jats:bold>\n                    that optimizes the flow continuity of subsequent edit suggestions based on developers\u2019 ongoing flow.\n                  <\/jats:p>\n                  <jats:p>\n                    Evaluations across diverse benchmarks, including manually annotated commits, real-world industrial code, and open-source repositories, show that EditFlow improves order reconstruction accuracy by 63.81%, reduces flow violations by over 75%, and boosts recommendation precision by 66.99%. A user study with 32 developers further demonstrates 25.11% faster task completion and significantly higher perceived recommendation quality. To the best of our knowledge, EditFlow is the first to evaluate and optimize code edit recommendation systems from the perspective of developers\u2019 mental flow, establishing\n                    <jats:italic toggle=\"yes\">flow-awareness<\/jats:italic>\n                    as a new dimension for advancing human-AI code collaboration.\n                  <\/jats:p>","DOI":"10.1145\/3798249","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T14:33:43Z","timestamp":1775831623000},"page":"1348-1375","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["EditFlow: Benchmarking and Optimizing Code Edit Recommendation Systems via Reconstruction of Developer Flows"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0554-4028","authenticated-orcid":false,"given":"Chenyan","family":"Liu","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"},{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8255-0118","authenticated-orcid":false,"given":"Yun","family":"Lin","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0199-1059","authenticated-orcid":false,"given":"Jiaxin","family":"Chang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9823-0538","authenticated-orcid":false,"given":"Jiawei","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0828-5544","authenticated-orcid":false,"given":"Binhang","family":"Qi","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1080-3278","authenticated-orcid":false,"given":"Bo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Bytedance Network Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1931-7775","authenticated-orcid":false,"given":"Zhiyong","family":"Huang","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6512-8326","authenticated-orcid":false,"given":"Jin Song","family":"Dong","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2025. 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