應用Llama 2於產品生產測試案例異常事件要因之預測分析－以桌上型工作站電腦為例

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施佳妏(Jia-Wun Shih) 查詢紙本館藏

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論文名稱

應用Llama 2於產品生產測試案例異常事件要因之預測分析－以桌上型工作站電腦為例
(Applying Llama 2 in Predictive Analysis of Causes for Anomalies in Test Cases of Product Manufacturing Process - A Case Study of Desktop Workstation Computer Products)

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至系統瀏覽論文 (2029-6-30以後開放)

摘要(中)

近年來，隨著電腦需求市場的快速變化，產品測試和驗證階段至關重要，以確保產品功能的正常運作。然而，現有的問題追蹤系統在查詢異常問題時僅能使用關鍵字，且搜尋出來的結果需人工逐筆過濾與判斷，導致效率低下且耗費大量時間。在快速發展的人工智慧技術中，生成式AI因其在文字處理上展現了驚人的創造力與對自然語言的理解能力，在醫療、金融等領域被快速採用。然而在過去研究中較少有研究將大型語言模型(Large Language Model, LLM)應用於電腦製造業中，因此本研究將針對電腦產品之測試案例所產生的異常事件，應用LLM建置一個要因預測分析系統，並評估零樣本與少樣本提示工程在回答上的表現，使LLM在電腦產品測試領域中能夠更有效地理解專業知識。該系統將允許測試人員以自然語言提問，系統將生成可能的要因與解決方案，提供測試人員未知或未注意到的觀點和思路，特別是針對較無經驗之新進人員，能夠協助其更快速地識別出問題發生之要因，同時達到組織內部的知識共享。

摘要(英)

Product testing and verification have become essential to ensuring the proper functioning of products. However, current issue tracking systems only allow users to employ keywords to query abnormal issues, and results still need to be manually filtered and judged by an expert, leading to low efficiency and taking considerable time. Generative artificial intelligence has been adopted in several industries, including health care and finance, due to its creativity and ability to understand natural language; however, large language models (LLMs), a type of generative artificial intelligence, have not yet been extensively applied in the computer manufacturing industry. The present study implemented Llama 2 to build a cause prediction analysis system that can predict abnormalities in computer products. At the same time, this study evaluates the performance of zero-shot and few-shot prompt engineering to enhance its understanding of domain-specific knowledge. With the LLM, a tester can ask questions using natural language, and the LLM generates possible causes and solutions, providing perspectives and ideas that the tester may be unaware of or have overlooked. The system is especially beneficial for newcomers—it helps them rapidly identify the causes of issues and facilitates knowledge sharing within an organization.

關鍵字(中)

★ 大型語言模型
★ 提示工程
★ 測試案例
★ 異常事件
★ 要因預測分析

關鍵字(英)

★ Large Language Model
★ Prompt Engineering
★ Test Case
★ Abnormality
★ Cause Prediction and Analysis

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
一、緒論 1
1-1研究背景 1
1-2研究動機 2
1-3研究目的 4
1-4研究範圍 6
1-5研究架構 7
二、文獻探討 8
2-1要因預測分析(Cause Analysis and Prediction) 8
2-2基於檢索的問答系統 (Retrieval-based Question Answering System) 10
2-3大型語言模型(Large Language Model, LLM) 12
2-4提示工程(Prompt Engineering) 16
三、系統設計 20
3-1系統架構 20
3-2系統設計 21
3-3提示設計 26
四、系統實作與展示 28
4-1系統開發環境 28
4-2系統展示 28
五、系統成果與討論 35
5-1系統成效 35
5-2使用者驗證 37
5-2-1訪談設計 38
5-2-2訪談結果 40
5-2-3問卷設計 45
5-2-4問卷結果 46
5-3與ChatGPT比較分析 47
5-4研究效度驗證 49
六、結論與未來研究方向 52
6-1研究貢獻 52
6-2研究限制與未來研究方向 53
參考文獻 55

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指導教授

陳仲儼(Chung-Yang Chen)

審核日期

2024-7-1

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