###### Abstract  
Fault localization, a critical process of software debugging, can be implemented by ranking program statements according to their suspiciousness of being faulty, which, in turn, is calculated based on the execution behaviors of test cases. The performance of fault localization will deteriorate if the actual faulty statement is ranked low in suspiciousness. Intuitively speaking, the quality of the used test cases affects the suspiciousness ranking and thus the efficacy of fault localization. As such, it is necessary to generate test cases with \"better\" quality to improve the chance of faulty statements being ranked as highly suspicious. In this paper, we propose a software fault localization approach based on feedback ranking information, namely FLFR, according to an improved genetic algorithm. The starting point of the new method is the execution of a set of test cases, which gives a preliminary suspiciousness ranking of program statements. The improved genetic algorithm is iteratively applied to generate new test cases. The new method is evaluated through a series of experiments on four C  
method, which can obtain the coverage information of each executable statement, and the execution result (pass or fail). [PERSON] et al. [5] presented a technique that obtained a ranking of statements in terms of their likelihood of being faulty by leveraging the result of combinatorial testing. Due to its effectiveness, SBFL has been used by developers for debugging in practice [6; 7]. In addition, there are a few software fault localization studies with deep learning techniques. For example, [PERSON] et al. [8] proposed a convolutional neural network-based fault localization method CNNFL (Convolutional Neural Network Fault localization). However, the performance of fault localization will be deteriorated if the actual faulty statement is ranked low in suspiciousness. Intuitively speaking, the quality of the used test cases affects the suspiciousness ranking and thus the efficacy of fault localization. As such, it is necessary to generate test cases with \"better\" quality to improve the chance of faulty statements being ranked as highly suspicious. To this end, some researchers have improved the efficiency of SBFL from the perspective of identifying coincidental correctness (CC) test cases [9; 10; 11; 12; 13]. Coincidental correct test cases have been investigated with negative effects on coverage-based fault localization, which implement the faulty statement but with correct output. [PERSON] et al. [13] conducted an empirical study on the impact of coincidental test cases on three tasks, namely test case reduction, test case prioritization, and SBFL. Results show that when all CC test cases were deleted, the effect of fault localization is improved. In addition, they mentioned in the threat analysis that clearing all coincidental test cases in multi-error situations reduces the effect of error localization. Existing methods often rely heavily on the quality and comprehensiveness of test cases. If the test cases are not comprehensive or do not cover all possible scenarios, the method may fail to locate the fault. In addition, many methods are based on statistical analysis and lack a deep understanding of the context and semantics of the software. This often leads to misinterpretations of the fault's causes and effects. Considering that the effectiveness of the SBFL technique is closely related to the test cases, it may have a positive effect from the perspective of increasing the test cases. To this end, we propose a software fault localization approach based on feedback ranking information (FLFR), which can help to generate test cases based on fault localization intermediate result information. The goal is to increase the rank of the statement containing the fault in the set of suspected faults. FLFR is designed to be highly accurate in identifying the source of software faults. They utilize user feedback and historical data to narrow down the possible location of faults.