TY - JOUR
T1 - Enhancing bug localization with bug report decomposition and code hierarchical network
AU - Zhu, Ziye
AU - Tong, Hanghang
AU - Wang, Yu
AU - Li, Yun
N1 - Funding Information:
This work is partially supported by the National Natural Science Foundation of China (No. 61772284 ), and the State Key Lab. for Novel Software Technology, China ( KFKT2020B21 ). Hanghang Tong is partially supported by NSF, USA ( 1947135 , 2134079 , and 1939725 ).
Funding Information:
This work is partially supported by the National Natural Science Foundation of China (No. 61772284), and the State Key Lab. for Novel Software Technology, China (KFKT2020B21). Hanghang Tong is partially supported by NSF, USA (1947135, 2134079, and 1939725).
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/7/19
Y1 - 2022/7/19
N2 - Bug localization, which aims to locate buggy source code files for given bug reports, is a crucial yet challenging software-mining task. Despite remarkable success, the state of the art falls short in handling (1) bug reports with diverse characteristics and (2) programs with wildly different behaviors. In response, this paper proposes a graph-based neural model BLOCO for automated bug localization. To be specific, our proposed model decomposes bug reports into several bug clues to capture bug-related information from various perspectives for highly diverse bug reports. To understand the program in depth, we first design a code hierarchical network structure, Code-NoN, based on basic blocks to represent source code files. Correspondingly, a multilayer graph neural network is tailored to capture program behaviors from the Code-NoN structure of each source code file. Finally, BLOCO further incorporates a bi-affine classifier to comprehensively predict the relationship between the bug reports and source files. Extensive experiments on five large-scale real-world projects demonstrate that the proposed model significantly outperforms existing techniques.
AB - Bug localization, which aims to locate buggy source code files for given bug reports, is a crucial yet challenging software-mining task. Despite remarkable success, the state of the art falls short in handling (1) bug reports with diverse characteristics and (2) programs with wildly different behaviors. In response, this paper proposes a graph-based neural model BLOCO for automated bug localization. To be specific, our proposed model decomposes bug reports into several bug clues to capture bug-related information from various perspectives for highly diverse bug reports. To understand the program in depth, we first design a code hierarchical network structure, Code-NoN, based on basic blocks to represent source code files. Correspondingly, a multilayer graph neural network is tailored to capture program behaviors from the Code-NoN structure of each source code file. Finally, BLOCO further incorporates a bi-affine classifier to comprehensively predict the relationship between the bug reports and source files. Extensive experiments on five large-scale real-world projects demonstrate that the proposed model significantly outperforms existing techniques.
KW - Bug localization
KW - Bug report
KW - Hierarchical network
KW - Network of networks
KW - Program behavior
KW - Software mining
UR - http://www.scopus.com/inward/record.url?scp=85129970669&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85129970669&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2022.108741
DO - 10.1016/j.knosys.2022.108741
M3 - Article
AN - SCOPUS:85129970669
SN - 0950-7051
VL - 248
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
M1 - 108741
ER -