# Log-Based Fault Localization with Unsupervised Log Segmentation
## 3 Results
In the timestamp ranking, 455 different log lines were to check (Table 6). The reduction in the number of log lines for these analyses was 92.31% (Figure 8). In Example 2, with context-based ranking in the first 285 segments, 320 log lines were for verification. With timestamp ranking, 3576 log lines were to be checked (Table 6). The reduction in the number of distinct templates for these analyses was 91.05% (Figure 8b). In Example 3, with context-based ranking, 34 log lines were to be verified in the first 32 segments. Using timestamp ranking, 47 log lines were to be checked (Figure 8a). The reduction in the number of distinct templates for these analyses was 27.65% (Figure 8b). In all cases, our solution outperformed the standard timestamp approach, providing the possibility of identifying the most important log lines more quickly, as presented in Table 5.  
line. This demonstrates that log-based methods can be applied in situations with large differences in log templates between normal and failed logs, where traditional LBFL returns a substantial portion of the same rank 1 log templates for analysis, and context can be used to refine the analysis. Our approach uses the context of log templates obtained from unsupervised log sequence segmentation, showing that the segmentation of log sequences can be useful for downstream tasks, such as fault localization. For further research, it would be interesting to investigate how the quality of the Drain influences the results. Because Drain is the main source of information regarding log templates and is highly dependent on the quality and accuracy of regular expressions, it would also be valuable to experiment by substituting this method with neural-based semantic approaches that are less reliant on human effort. Further experiments can also be conducted using sophisticated neural-based methods to compare segments, including Transformer-based methods. Conceptualization, W.D. ; methodology, W.D. and O.U. ; software, W.D. and K.I.-K.; validation, W.D. and K.I.-K.; formal analysis, O.U. ; investigation, W.D. ; resources, O.U. ; data curation, W.D. ; writing--original draft preparation, W.D. ; writing--review and editing, M.N. and W.D. ; visualization, W.D. ; supervision, O.U. ; project administration, W.D. ; funding acquisition, O.U. All authors have read and agreed to the published version of the manuscript. The Polish Ministry of Education and Science financed this work. Funds were allocated from the \"Implementation Doctorate\" program. Funding number is SD1/DWD/4/13/2020/STYP. Not applicable. Data Availability Statement: Data are available in a publicly accessible repository [[https://github.com/dobrowol/log_based_fault_localization](https://github.com/dobrowol/log_based_fault_localization)]([https://github.com/dobrowol/log_based_fault_localization](https://github.com/dobrowol/log_based_fault_localization)) (accessed on 16 September 2024). Authors [PERSON] and [PERSON] were employed by the company Nokia. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The following abbreviations are used in this manuscript:  
SBFL Spectrum-Based Fault Localization  
KPI Key Performance Indicators