本論文使用 sequential pattern mining 方法分析 openssh 程式之動態歷程，輔以在 openssh 版本歷程中的變動情況，以描述程式元素(program element)之關係，其中使用 Support 用以挑選出現次數夠高的 pattern，接著以 Confidence 決定 pattern 之品質，其品 質可分為三類:
z frequent： 該 pattern 於執行中完全無例外情況，可做為使用建議
z Potential Error：該 pattern 於執行中有少數例外情況，可檢視該例外情況以除錯 z Unlikely： 該 pattern 於執行中有許多例外情況，pattern 可能實際上不存在 最後以 Variant 比較不同 pattern 的重要性，以及 pattern 中各單元的重要程度。
本研究有二個主要頁獻，其一是相較其它論文所使用的 apriori-based 之方法，使用 sequential pattern 方法，可以確定 pattern 中先元素的前後次序；二是導入變動情況的概 念，故可以更明確地描述 pattern，並顯示出易錯誤處。
5.1. 與相關研究之比較
分析實體 分析範圍 特點比較
Guide Software Changes (2004)
Each revision diff
• Less dedicated program analysis
System specific Rule (2005)
Procedure call
10 lines in a revision
• Pairwise pattern
• Static analysis Matching Method
calls (2005)
Procedure call
Each revision diff
• Pairwise pattern
• Dynamic Analysis PR-Miner (2005) Procedure
call and variable
intra-procedure • Static analysis
Our work Procedure call and variable
intra-procedure • Dynamic analysis
• Ordered pattern
5.2. 未來改進方向
5.2.1. 偵測異名變數
C 語言由於其指標(pointer)便於使用，因而功能強大，然而指標的使用卻帶來程式分 析時相當大的困擾。以下列三行 C 程式為例說明：
int * flag1 = 3;
int * flag2;
flag2 = flag1;
在執行後，記憶體內的有一塊單元其值為三，而 flag1 及 flag2 皆指向此位址，使用指標 透過 flag1 修改該單元的值也會造成 flag2 指向的值改變，反之亦然，因此在此 flag1 及 flag2 具有相同之意義，然而在本論文中 flag1 及 flag2 是視為不同的，例如:
foo(flag1);
以及
foo(flag２);
將會被統計為不同的 pattern，由於該問題為 NP-Complete program，因此若加入 pointer alias analysis 之近似演算法應可以部份解決此問題。以上的問題可能造 成 false-negative 之情形（即存在的規則被忽略而沒被找出）。
5.2.2. 改善版本變動歸因
由於本研究在處理版本資料時皆只使用一行修正(one-line check-ins)，若有一組重要 的函式使用方式是從未出錯或恰好其修改皆在二行以上，則我們的研究將無法彰顯這些 函式的重要性。若能有更細緻的演算法標定每次的版本更動的性質，則能更精確描述每 個 pattern 之變動性或重要性。
5.2.3. 利用資料相依性
本研究僅使用變數更名的方式以表達資料相依性(data dependency)，然而此種簡易 的方法仍不足我們的需求，若能透過變數展現函式間的關係，搭配前文之 sequential mining 之方法，則有助於找出低發生次數的 pattern，並降低 false-pattern 的機會
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