# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 1 Introduction  
In the modern era, software systems have evolved to become increasingly complex, integrating a wide range of functionalities through intricate source code structures. When unexpected bugs emerge, the debugging process typically involves locating and rectifying faulty source code. This process, however, often requires developers to manually examine bug reports and navigate extensive codebases, making it both time-consuming and resource-intensive. Existing methods for bug localization aim to alleviate this challenge, but significant gaps remain in their accuracy, scalability, and adaptability across diverse datasets.  
Several studies have explored bug localization techniques to enhance this process. [PERSON] et al. [1] proposed leveraging the sequential characteristics of source code by integrating convolutional neural networks (CNNs) and long short-term memory (LSTM) algorithms torecommend buggy source code files. Similarly, [PERSON] et al. [2] employed a revised Vector Space Model (rVSM) to measure text similarities between bug reports and source files, combined with a deep neural network (DNN) to improve the correlation between bug reports and source code. [PERSON] et al. [3] introduced an adaptive ranking model based on domain knowledge and text similarity, incorporating software engineering features such as bug-fixing history and kernel scores. [PERSON] et al. [4] developed BugLocator, which utilizes similar bug reports to compute suspicion scores between bug reports and source files. Additionally, [PERSON] et al. [5] proposed a novel bug localization method that combines structured information and historical code changes to identify buggy files.  
While these approaches have advanced the field of bug localization, limitations persist. Most methods require further validation on diverse datasets and often struggle to achieve consistent performance across different software projects. Additionally, the computational complexity of some algorithms poses challenges for real-time applications. To address these issues, this study introduces a new buggy file recommendation approach that combines the LSTM-Attention mechanism with top-K code similarity learning.  
The proposed method first preprocesses bug reports and program source code files to extract relevant features. Historical codes that exhibit high similarity to a given bug report are identified using the BM25 algorithm. These top-K code candidates are then fed into the LSTM-Attention algorithm, which learns the contextual relationships between bug reports and source code features to predict the relevant buggy file.  
To evaluate the effectiveness of this method, we conducted comparative experiments using DNNLoc [2] as the baseline model. The proposed approach showed significant performance improvements, achieving an average accuracy of approximately 79.09% across six open-source projects, representing a 17.54% improvement over the baseline. These findings underscore the potential of integrating LSTM-Attention with top-K code similarity learning to enhance bug localization accuracy and efficiency.  
The key contributions of this paper are as follows:  
* A novel buggy file recommendation system that integrates LSTM-Attention and top-K code similarity learning, validated against existing methods.
* A performance comparison with the DNNLoc baseline shows superior bug localization accuracy and efficiency.
* Practical implications for improving software quality by automating the bug localization process, reducing developer effort, and minimizing debugging costs.  
Through this approach, this study aims to streamline debugging workflows and advance the field of automated bug localization, contributing to the broader goals of software reliability and maintainability.