在軟體開發的過程當中，為了修復、重構或是增加功能，工程師會不斷的修改既有的程式碼。為了確保在每一次改動之後的軟體品質，回歸測試非常重要。為了讓測試人員進行回歸測試更有效率，本實驗室開發了一套錄製/播放類的測試工具－Korat。
Korat為了達到非平台相依，使用影像辨識來偵測目標，在非Windows平台時，我們透過影像擷取卡取得待測系統的畫面，作為影像辨識的影像來源。我們發現像是Korat這種基於電腦視覺辨識目標類型的工具軟體，在特定狀況的GUI，像是反鋸齒差異、目標影像發生變化或是影像擷取卡雜訊干擾，造成測試當下的影像與錄製時不同，無法正常回放測試案例。
本論文中我們將針對Korat這個錄製/播放類的測試工具，詳細說明無法正確執行測試案例特定的狀況，並提出自動學習修正腳本方法，自動增加目標圖片，並利用影像辨識結果聯集的方式，減少受到上述問題的影響，最後說明未來能夠改進的方向。

In the process of software development, in order to repair, refactor or add functions, engineers will constantly modify the existing code. To ensure the quality of the software after every change, regression testing is very important. so as to allow testers to perform regression testing more efficiently, the laboratory has developed a set of recording/playback testing tool - Korat.
For achieve non-platform dependence, Korat uses image recognition to detect targets. On non-Windows platforms, we use the image capture card to obtain the screen of the system under test as the image source for image recognition. We found that tools such as Korat, which are based on computer vision to identify target, have GUIs in specific situations, such as anti-aliasing, target image changes, or image capture card signal noise interference, resulting in the current test image and recording test image is different, the test case cannot be played back normally.
In this thesis, we will explain the specific situation of the test case that cannot be correctly executed for recording/playback test tool Korat, and propose an automatic learning and correction script method to automatically increase the target picture and use the method of image recognition result set. Reduce the impact of the above problems, and finally explain the direction that can be improved in the future.

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