## 4 Experiments
### Results  
CodeTransFix was initially compared with state-of-the-art NMT-based APR methods and LLMCs for bug fixes under perfect fault localization settings. The performance of CodeTransFix and other baselines that also employ perfect fault localization is displayed in Table 1. Specifically, CodeTransFix generated 37 correct patches on the Defects4j v1.2 benchmark. CodeTransFix fixed 13 more bugs compared to the best NMT-based APR method and 7 more bugs compared to the best-performing LLMCs. The extent to which CodeTransFix is able to complement existing NMT-based methods and approaches for buggy code repair using LLMCs is studied in further detail. In Figure 5, to explicitly demonstrate our method's unique repair characteristics, the NMT-based APR approaches encompass three conventional systems (CURE [16], Recoder [12], and Reward [17]); LLMCs feature scaled implementations where CodeT5 [7] employs both small and base parameterizations while PLBART [8] adopts base and large model configurations. Figure 5 illustrates the overlap of bugs fixed using the different approaches. As shown in the figure, CodeTransFix fixes eight unique bugs. This finding indicates that it can be used in conjunction with other methods to significantly boost the number of correct patches produced. Below, we have provided some examples of CodeTransFix fixing bugs. Unlike compiler diagnostics that detect lexical and syntactic errors (e.g., missing semicolons) or basic semantic violations (e.g., type mismatches), our approach specifically targets logic-driven defects that pass compilation but exhibit incorrect runtime behaviors, such as improper API usages or flawed control flow logic. Figure 5(a) shows how CodeTransFix fixes a Math 41 bug that stems from the improper setting of the start value and termination condition in a loop statement. It is based on the fixes in the historical data that CodeTransFix was able to successfully fix this type of Math 41 bug (as shown in Figure 5(b)). Figure 5(c) shows an example of CodeTransFix successfully fixing the Closure 123 bug that other APR methods failed to fix. In the original buggy code, the assignment statement of the CodeGenerator class directly uses the enumerated member Context.OTHER for static \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Projects**} & \multirow{2}{*}{**CodeTransFix**} & \multicolumn{2}{c}{**PLBART**} & \multicolumn{2}{c}{**CodeT5**} & \multicolumn{2}{c}{**NMT-Based APR Techniques**} \\ \cline{3-8}  & & **Base** & **Large** & **Small** & **Base** & **CURE** & **Recoder** & **Reward** \\ \hline Chart & 3 & 3 & 4 & 2 & 4 & 0 & 6 & 2 \\ Closure & 15 & 6 & 5 & 2 & 6 & 2 & 5 & 4 \\ Lang & 4 & 2 & 4 & 3 & 5 & 1 & 3 & 5 \\ Math & 15 & 11 & 13 & 9 & 13 & 2 & 9 & 6 \\ Mockito & 0 & 2 & 2 & 2 & 1 & 0 & 1 & 2 \\ Time & 1 & 1 & 2 & 1 & 1 & 1 & 0 & 1 \\ Total & 37 & 25 & 30 & 19 & 30 & 6 & 24 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Repair performance comparison between CodeTransFix and existing baselines under perfect fault localization settings. state identification, which causes the test case to fail. The correct fix requires calling the getContextForNoInOperator method, also belonging to the CodeGenerator class, which dynamically returns the appropriate state by analyzing the current scope for the inclusion of the 'in' operator (indicated by the parameter context). CodeTransFix achieves the correct fix by analyzing the context of the defective code and recognizing that the function should be called instead of hard-coding the enumeration value. In order to confirm that CodeTransFix can be used to fix defects in other projects and that it does not simply overfit Defects4j v1.2 bugs, it was tested on 82 single-line bugs from the Defects4j v2.0 benchmark. The comparative findings with different baselines on Defects4j v2.0 are displayed in Table 2. As seen in Table 2, all three methods fix a smaller number of bugs on Defects4j v2.0, indicating that it is more difficult to fix bugs on Defects4j v2.0.