# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 6 Discussion  
### Experiment Result  
The experimental results provide strong evidence that the proposed LSTM-Attention-based bug localization method outperforms existing baseline approaches. By integrating top-K code similarity learning with deep learning-based prediction, the model significantly improves the accuracy of buggy file identification across multiple open-source projects. The effectiveness of the proposed approach is evident in its ability to enhance both retrieval accuracy and ranking precision. A key component of this improvement is the utilization of top-K code similarity learning, which refines the selection of candidate source code files before applying the LSTM-Attention mechanism. The comparative analysis of different K values shows the impact of this technique on model performance. When top-K similarity learning was not applied, the model achieved an accuracy of 68.60%. When K = 1, the performance improved to 71.08%, indicating that retrieving at least one highly similar code file contributes to better bug localization. Further increasing K to 2 resulted in an accuracy of 79.09%, confirming that retrieving a broader but relevant set of source code files enhances the predictive capability of the model. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Hypothesis** & \(\mathbf{p}\)**-Value** & **Result** \\ \hline H1\({}_{0}\) & (\(t\)-test) \(1.11\times 10^{-5}\) & H1a: Accept \\ \hline H2\({}_{0}\) & (\(t\)-test) 3.36 \(\times\) 10\({}^{-6}\) & H2a: Accept \\ \hline H3\({}_{0}\) & (\(t\)-test) 1.38 \(\times\) 10\({}^{-6}\) & H3a: Accept \\ \hline H4\({}_{0}\) & (\(t\)-test) 4.51 \(\times\) 10\({}^{-7}\) & H4a: Accept \\ \hline H5\({}_{0}\) & (\(t\)-test) 1.06 \(\times\) 10\({}^{-6}\) & H5a: Accept \\ \hline H6\({}_{0}\) & (\(t\)-test) 2.83 \(\times\) 10\({}^{-8}\) & H6a: Accept \\ \hline H7\({}_{0}\) & (\(t\)-test) 7.90 \(\times\) 10\({}^{-9}\) & H7a: Accept \\ \hline H8\({}_{0}\) & (\(t\)-test) 1.83 \(\times\) 10\({}^{-7}\) & H8a: Accept \\ \hline H9\({}_{0}\) & (\(t\)-test) 1.64 \(\times\) 10\({}^{-7}\) & H9a: Accept \\ \hline H10\({}_{0}\) & (\(t\)-test) 2.22 \(\times\) 10\({}^{-8}\) & H10a: Accept \\ \hline H11\({}_{0}\) & (\(t\)-test) 2.66 \(\times\) 10\({}^{-8}\) & H11a: Accept \\ \hline H12\({}_{0}\) & (\(t\)-test) 2.58 \(\times\) 10\({}^{-7}\) & H12a: Accept \\ \hline H13\({}_{0}\) & (\(t\)-test) 2.48 \(\times\) 10\({}^{-7}\) & H13a: Accept \\ \hline H14\({}_{0}\) & (\(t\)-test) 2.44 \(\times\) 10\({}^{-8}\) & H14a: Accept \\ \hline H15\({}_{0}\) & (Wilcox test) 1.56 \(\times\) 10\({}^{-2}\) & H15a: Accept \\ \hline H16\({}_{0}\) & (\(t\)-test) 8.93 \(\times\) 10\({}^{-7}\) & H16a: Accept \\ \hline H17\({}_{0}\) & (\(t\)-test) 6.96 \(\times\) 10\({}^{-5}\) & H17a: Accept \\ \hline H18\({}_{0}\) & (\(t\)-test) 1.63 \(\times\) 10\({}^{-7}\) & H18a: Accept \\ \hline H19\({}_{0}\) & (\(t\)-test) 2.22 \(\times\) 10\({}^{-7}\) & H19a: Accept \\ \hline H20\({}_{0}\) & (\(t\)-test) 8.24 \(\times\) 10\({}^{-6}\) & H20a: Accept \\ \hline H21\({}_{0}\) & (\(t\)-test) 1.15 \(\times\) 10\({}^{-3}\) & H21a: Accept \\ \hline H22\({}_{0}\) & (\(t\)-test) 8.98 \(\times\) 10\({}^{-6}\) & H22a: Accept \\ \hline H23\({}_{0}\) & (\(t\)-test) 9.39 \(\times\) 10\({}^{-9}\) & H23a: Accept \\ \hline H24\({}_{0}\) & (\(t\)-test) 1.13 \(\times\) 10\({}^{-7}\) & H24a: Accept \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical Verification Results. The performance comparisons also reinforce the necessity of top-K code similarity learning in bug localization tasks. The experimental results show that the model with top-K similarity learning consistently outperforms its counterpart without this feature, validating its effectiveness in improving buggy file prediction. The inclusion of similar source code files as input allows the model to leverage textual and contextual patterns between bug reports and code repositories, leading to more accurate localization results.