Automatically correcting software bugs is not an easy job because the process of repairing bugs contains many uncertainties. As the size and complexity of software increases, manually correcting software bugs becomes very difficult. Hence, automatic software repair is becoming more and more essential. Genetic programming (GP) is a great method for addressing this problem and a great deal of research has used GP to find ways the repair faulty programs in recent years. Nevertheless, most of variants generated by GP are not precise to detect solution repair.
In this thesis, we propose a fault-based genetic-like programming approach that heuristically searches all possible variants as they increasing with the number of modifications. Our method is able to find the best repairs for programs with few faults faster than genetic programming. However, the penalty is that heuristically searching for suitable repairs takes too much time. Hence, we also optimized our approach to speed up the performance.
In this study, our approach was used to repair faulty C programs and the results were compared with those generated by genetic programming. The results show that our approach was able to better detect faulty programs in up to 18000 lines of code when the number of program faults was less than two.

自動修復軟體的錯誤並不是一件容易的事，因為在過程中會有許多的不確定性在裡面。隨著軟體的大小和複雜度逐漸地增加，人工的去修復軟體的錯誤變的更加的困難，進而使得自動軟體修復的重要性也將變得越來越大。針對這個問題，基因規劃(GP) 是一個很好的方法，近幾年來有很多的研究已經運用此種方法去修正程式的錯誤。然而，這個方法所產生的程式變異體並不是針對程式的錯誤去進行變異的。
在本篇論文中，我們提出了一種基於故障之類基因規劃方法去隨著修改的數量啟發式地尋找所有可能的變異體。我們的方法可以比運用基因規劃更快的在錯誤不多的程式上找到最好的修改。但是，啟發式地搜尋合適的修復之耗費很大。因此，我們對於我們的方法也做了一些最佳化處理來改善這個缺點。
此外，我們將這個方法運用於修復C的程式上並和基因規劃產生的結果互相比較。經比較後發現，我們的方法擁有比基因規劃更好的能力去修復程式碼高達一千八百行並且少於兩個錯誤的程式。

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