近年來，大型語言模型與 Web Agent 技術逐漸被應用於端對端（end-to-end， E2E） 網站測試，使自動化流程由傳統腳本撰寫進一步延伸至網站功能推論、測試案例生成 與自然語言任務執行。若希望將自動化範圍進一步延伸至測試案例建立之前的網站 理解階段，則不能假設完整需求文件、既有測試案例、內部程式資訊或人工預先定義 的 E2E 任務一定會作為必要測試輸入。在此設定下仍存在三項前置問題：黑箱探索結 果多以頁面狀態、事件轉移與操作路徑呈現，尚未直接形成使用者層級的 E2E 任務； Web Agent 測試通常假設自然語言任務已經存在；既有 LLM-based E2E test generation 則 主要關注功能推論與測試案例生成，而非確認任務在目前網站版本中是否實際達到完 成狀態。 因此，本研究提出一套從黑箱網站自動盤點 E2E 任務，並判定其前端可觀察完 成狀態的方法。網站入口 URL 僅作為啟動黑箱探索的定位資訊，並可選擇提供測試 帳號或既有登入狀態；後續任務建立所需的功能資訊，則由系統在實際探索受測網 站時動態取得。系統首先使用 Playwright 蒐集 URL ／ DOM 狀態、可互動元素、表單 affordance、操作前後變化與探索路徑；接著以探索路徑為分析單位，透過大型語言模 型逐步抽取包含使用者目標與必要步驟的候選 E2E 任務，並進行跨路徑合併與去重。 任務建立後，系統依任務步驟與既有探索證據的對齊程度進行分流：證據充分者直 接進入完成性判定，證據不足者才交由 Web Agent 補充執行。最後，系統整合爬蟲與 Web Agent 的結構化互動證據，將任務判定為 pass、partial、fail 或 blocked，並輸 出受影響或受阻步驟、失敗原因與可追溯 evidence pack。系統亦可依任務步驟產生初步 Playwright pytest 腳本骨架，但該腳本僅作為人工後續修訂的輔助輸出。 本研究以 PrestaShop、Spring PetClinic 與 Conduit 三個開源網站進行評估。任務盤 點實驗中，本研究方法的 macro-average Precision、Recall 與 F1 分別為 0.8646、0.8618 與 0.8543，高於 AutoE2E 基準方法的 0.6952、0.6306 與 0.6519。正常版本完成性判定 i 實驗共評估 144 個 E2E 任務，其中 111 個任務被判定為 pass，Pass Rate 為 77%；透過 證據分流，僅 64 個任務需要 Web Agent 補驗，相較於所有任務皆交由 Agent 執行，可 減少 55.6% 的 Web Agent 執行需求。故障注入實驗共建立十二個前端可觀察故障案例， 系統在其中八個案例找到受影響任務，並於五個案例正確回報 fail。結果顯示，本研 究可在設定的黑箱探索範圍內，將前端探索證據整理為可追溯的 E2E 任務集合，並提 供任務層級的前端可觀察完成性判定；其限制主要在於未被探索的功能與無法由前端 觀察之後端或純視覺正確性。;Recent advances in large language models and Web Agents have extended end-to-end (E2E) web testing automation beyond conventional script authoring toward feature inference， test-case generation， and natural-language task execution. To further automate the application- understanding stage that precedes test-case authoring， a testing workflow cannot assume that complete requirements， existing test cases， internal implementation information， or manually predefined E2E tasks are always available as mandatory inputs. Under this setting， three is- sues remain. First， black-box exploration primarily produces page states， event transitions， and navigation paths rather than user-level E2E tasks. Second， Web-Agent-based testing generally assumes that a natural-language task or test case has already been defined. Third， existing LLM- based E2E test generation focuses primarily on feature inference and test generation rather than verifying whether a generated task can actually reach a completion state in the current applica- tion version. This study therefore proposes a method for automatically inventorying E2E tasks from a black-box web application and determining their frontend-observable completion states. The website entry URL serves only as the locator that initiates exploration， with an optional test account or authenticated browser state; the functional information required for subsequent task construction is acquired dynamically by interacting with the system under test. Playwright is first used to collect URL/DOM states， interactive elements， form affordances， before/after interaction changes， and navigation paths. An LLM then progressively extracts candidate E2E tasks from each exploration path， representing each task with a user goal and essential steps， followed by cross-path semantic merging and deduplication. After task construction， the system triages tasks according to the alignment between task steps and existing exploration evidence. Tasks with sufficient evidence proceed directly to completion verification， whereas only evidence- insufficient tasks are assigned to a Web Agent for supplementary execution. Finally， the system integrates structured crawler and Web Agent evidence to classify each task as pass， partial， fail， or blocked， and reports affected or blocked steps， failure reasons， and a traceable evidence pack. Preliminary Playwright pytest script skeletons are also derived from task steps， but they are treated only as auxiliary outputs that require subsequent manual review and refinement. The proposed method is evaluated on three open-source web applications: PrestaShop， Spring PetClinic， and Conduit. For E2E task inventory， the proposed method achieves macro- average Precision， Recall， and F1 scores of 0.8646， 0.8618， and 0.8543， respectively， compared with 0.6952， 0.6306， and 0.6519 for the AutoE2E baseline under the same task-level alignment protocol. In the normal-version completion-verification experiment， 111 of 144 tasks are clas- sified as pass， corresponding to a 77% Pass Rate. Evidence-based triage sends only 64 of the 144 tasks to the Web Agent， reducing Web Agent execution demand by 55.6% relative to exe- cuting the Agent for every task. In twelve controlled frontend-observable fault-injection cases， the system discovers affected tasks in eight cases and correctly reports fail in five cases. These results show that， within the configured black-box exploration scope， the proposed method can transform frontend exploration evidence into a traceable E2E task inventory and provide task- level frontend-observable completion verdicts. Its main limitations are functions that are not reached during exploration and correctness properties that cannot be established from frontend- observable evidence alone.
