迴歸測試在軟體測試中扮演重要的角色，因為迴歸測試常常用來實行精簡或排序測試個案，並藉由不斷的重複類似的測試過程而取得最佳化的結果。測試個案精簡的方法通常是在一些程式碼或是分支覆蓋率等準則下進行。在軟體迴歸測試中，程式碼覆蓋率是很重要的因子。修改條件/判斷覆蓋是一種高精確度和嚴謹的程式碼覆蓋率準則，常用於需要高安全性的軟體系統上如飛航軟體或是銀行軟體等。在過往的研究中， Hayhurst等人曾經提出以修改條件/判斷覆蓋準則為基準下的測試方法，此種方法主要是用判斷表示式的方法來達到測試個案精簡的目的。然而此方法仍然有些瑕疵，像是不能找到某些重要且特定的錯誤類型。本研究主要是以貪婪演算法的應用來修改與改良Hayhurst的方法，使之更臻於完善。實驗方面，我們使用了四個不同來源的受測程式，並提出三種測試準則做為比較基礎。最後，我們以數種資料與實驗數據來證明我們實驗的內部有效性、外部有效性和結構有效性。

In general, regression testing plays an important role in software testing which can offer help to minimize test suite size or rearrange the test cases. In software regression testing, the code coverage of target program is a crucial factor while we perform test case reduction and prioritization. Modified Condition/Decision Coverage (MC/DC) is one of the most strict and high-accuracy criterion in code coverage. In previous research, Hayhurst et al proposed a method to implement the MC/DC criterion. However, there might be some flaws when using Hayhurst’s method to select test cases. In this study, we will give some examples to indicate the importance of some test cases which are considered as redundant test cases. Then we describe how to enhance Hayhurst’s method with a greedy-based algorithm. Finally, we perform an experiment with four target programs from three different sites to verify our proposed method, and further prove the construct, internal and external validity of our experiment.

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