# Software Fault Localization Based on SALSA Algorithm
## 1 Introduction  
In today's rapidly developing software industry, software debugging is a key link in ensuring software quality and reliability [1]. With the increasing complexity and expansion of software systems, the debugging process is becoming increasingly challenging. Software debugging not only helps developers identify and fix errors or defects in the program, but it is also essential for preventing potential system failures, improving user satisfaction, and reducing maintenance costs [2]. Effective software debugging strategies typically include fault localization, which is a core part of the debugging process. In practical software engineering applications, the efficiency and accuracy of fault localization significantly impacts the development cycle and costs. Rapid and accurate fault localization can substantially reduce the time developers spend on debugging, thereby accelerating software release schedules. Additionally, by minimizing system failures caused by errors, software stability and user satisfaction can be enhanced. Traditional fault localization methods often rely on developers' experience, intuition, and manual code reviews, which are not only time-consuming and labor-intensive but also prone to human error [3]. For instance, in large-scale software projects, evenminor errors can lead to system crashes or performance degradation, and traditional fault localization methods may take hours or even days to identify the root cause of the problem. To overcome these limitations, researchers have begun to explore automated and semi-automated fault localization techniques. Among these emerging technologies, software fault localization techniques based on spectrum have attracted widespread attention due to their unique advantages. Traditional spectrum-based software fault localization techniques determine the code areas that may contain defects by analyzing the coverage of the tested program after execution. Specifically, they utilize the execution information of the tested program (such as covered statements, branches, or paths) and the corresponding test results (success or failure), and assess the risk level of each statement through statistical methods [4]. The core idea of this method is that if a test case fails, the statements it covers are more likely to contain defects. However, Spectrum-Based Fault Localization (SBFL) suffers from the tie problem, where multiple statements within the same basic block may have the same suspicion value, making it impossible to locate the specific faulty statement in the program precisely. This situation severely affects the developers' debugging efficiency because it leads to a vague indication rather than a clear fault point. To address this issue, researchers have proposed various improvement methods, including methods based on metric combination [5], improving program matrices [6], considering fault propagation context [7], considering local dependency relationships [8], and utilizing program slicing [9; 10] to solve this problem. With the development of various technologies and tools, combining traditional software fault localization techniques and different methodologies is a mainstream direction of current research. Some researchers combine the trajectory of program execution with graph theory to study its network structure for precise localization. Based on the concepts of graph theory, various network analysis algorithms have been invented to analyze the structure of network links and assess the importance of nodes. In recent years, software fault localization techniques based on various network analysis algorithms have achieved certain effects. These methods use abstract syntax trees (AST) [11], Control Flow Graphs (CFG) [12], and Program Dependency Graphs (PDG) [13] as intermediate data representations, abstracting code structure into graph structures. However, when facing large-scale programs, directly constructing a graph from the source code can result in a very large graph, which can severely affect the efficiency of network structure analysis.