发表机构
Singapore University of Technology and Design（新加坡科技设计大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出TeamLens，一个基于同意的团队构成界面，支持自愿披露MBTI类型，并通过合成评估验证其正确性、隐私和扩展成本，贡献于设计协作中的披露到聚合工作流。
AI 中文摘要
讨论工作偏好可能支持设计团队内的反思，但个性标签不应成为绩效预测或参与条件。本技术报告介绍了TeamLens，一个可选的Critsly界面，用于在特定看板上自愿分享自我报告的MBTI类型。它将账户激活与披露分离，显示描述性构成计数，并提供禁用可见性、撤回一份报告和删除个人所有报告的独立控制。评估结合了发布源代码检查、独立指定的合成聚合案例、访问和生命周期检查、浏览器组件测试、本地数据库微基准测试和部署记录。16个固定、不同类型报告的所有65,536个二元资格子集均与独立预言机匹配；在4,259次合成分享调用后，128个种子多重性固定装置也匹配。隔离的HTTP套件通过了183个断言。在180次顺序内存SQLite读取试验中，中位服务调用时间从无配置文件行的0.064毫秒增加到1,024行的200.932毫秒；插桩SQL操作遵循11+3n。这些发现涉及已执行的软件行为和有限的工作负载。它们不确立人类可用性、心理测量有效性、学习收益、团队绩效效应或生产能力。贡献在于实现了一个从披露到聚合的工作流程，以及对其正确性边界、隐私限制和扩展成本的可审计技术说明。OpenAI Codex协助了实施、评估和手稿准备；本文披露了此使用及其限制。
英文摘要
Discussing working preferences may support reflection within a design team, but a personality label should not become a performance prediction or a condition of participation. This technical report presents TeamLens, an optional Critsly interface for voluntarily sharing a self-reported MBTI type with a particular board. It separates account activation from disclosure, displays descriptive composition counts, and provides distinct controls for disabling visibility, withdrawing one report and deleting all of one's reports. The evaluation combines released-source inspection, independently specified synthetic aggregation cases, access and lifecycle checks, browser component tests, a local database microbenchmark and deployment records. All 65,536 binary eligibility subsets of sixteen fixed, distinct type reports matched an independent oracle; 128 seeded multiplicity fixtures also matched, after 4,259 synthetic share calls. The isolated HTTP suite passed 183 assertions. In 180 sequential in-memory SQLite read trials, median service-call time increased from 0.064 ms with no profile rows to 200.932 ms with 1,024 rows; instrumented SQL operations followed 11+3n. These findings concern exercised software behaviour and a bounded workload. They do not establish human usability, psychometric validity, learning gains, team-performance effects or production capacity. The contribution is an implemented disclosure-to-aggregation workflow and an auditable technical account of its correctness boundaries, privacy limitations and scaling cost. OpenAI Codex assisted with implementation, evaluation and manuscript preparation; the paper discloses this use and its limits.
Comments16 pages, 8 figures, 6 tables. Technical software evaluation using synthetic fixtures; includes ancillary synthetic records and verification scripts
