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Analysis of Depth-First Search Based Mousavian Technique’s Scalability for Software Bug Localization
Fariz Tumbuan M.U.a, Ayu Putri Saptawati G.a
a Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Debugging in software development is one of the most time-consuming processes. To that end, various bug localization techniques have been developed. One approach showing great result is the graph mining-based bug localization techniques. Unfortunately, the graph mining algorithm tends to scale poorly when dealing with large graph input. Mousavian et al. developed a new bug localization technique that replaces the graph mining algorithm with a new, more scalable graph analyzing technique. However, the mousavian technique has not been tested using highly complex graphs as input. In addition, its suspicious paths generation algorithm was not explained in detail to enable complete implementation. The test result shows a suspicious paths generation algorithm can be implemented using a modified depth- first search algorithm. Furthermore, the mousavian technique can be categorized as a scalable bug localization technique because it has a polynomial growth pattern for both processing time and maximum memory usage. However, the proposed depth-first search algorithm shows an exponential growth pattern for its process time. The processing time of the depth-first search algorithm can be significantly reduced by limiting the depth-first search depth limit at the cost of information completeness.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bug localizations,Depth first search,Depth first search algorithms,Exponential growth,Generation algorithm,Information completeness,Modified depth first search algorithms,Polynomial growths[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]breadth-first search,depth-first search,graph mining,scalability,software bug localization,weighted graph[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICoDSE48700.2019.9092722[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]