TY - GEN
T1 - Assisting Bug Report Assignment Using Automated Fault Localisation
T2 - 14th IEEE International Conference on Software Testing, Verification and Validation, ICST 2021
AU - Sohn, Jeongju
AU - An, Gabin
AU - Hong, Jingun
AU - Hwang, Dongwon
AU - Yoo, Shin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - We present a case study of an industry scale application of automated fault localisation to SAP HANA2 database. When a test breaks in the Continuous Integration (CI) pipeline, the bug needs to be triaged and assigned to the appropriate development team. Given the scale and complexity of SAP HANA2, the assignment itself can be a challenging task. The current practice depends on the static mapping between test scripts and software components, as well as human domain knowledge. We apply automated fault localisation to aid the issue allocation in the CI pipeline: once a test failure is observed, the automated fault localisation technique identifies the suspicious software component using the information from the test failure. The localisation result can be used by the issue manager to allocate the incoming test failure issues more efficiently. We have analysed 137 CI test executions with at least one failing test script using Spectrum Based Fault Localisation. The results show that automated fault localisation can identify the faulty software component for 61 out of 137 studied test failures within top 10 places out of over 200 components. Out of the 61 faults, 36 faults were not identifiable based on the static mapping between test script and software components at all.
AB - We present a case study of an industry scale application of automated fault localisation to SAP HANA2 database. When a test breaks in the Continuous Integration (CI) pipeline, the bug needs to be triaged and assigned to the appropriate development team. Given the scale and complexity of SAP HANA2, the assignment itself can be a challenging task. The current practice depends on the static mapping between test scripts and software components, as well as human domain knowledge. We apply automated fault localisation to aid the issue allocation in the CI pipeline: once a test failure is observed, the automated fault localisation technique identifies the suspicious software component using the information from the test failure. The localisation result can be used by the issue manager to allocate the incoming test failure issues more efficiently. We have analysed 137 CI test executions with at least one failing test script using Spectrum Based Fault Localisation. The results show that automated fault localisation can identify the faulty software component for 61 out of 137 studied test failures within top 10 places out of over 200 components. Out of the 61 faults, 36 faults were not identifiable based on the static mapping between test script and software components at all.
KW - fault localisation
KW - spectrum based fault localisation
KW - test suite diagnosability
UR - https://www.scopus.com/pages/publications/85108015827
U2 - 10.1109/ICST49551.2021.00041
DO - 10.1109/ICST49551.2021.00041
M3 - Conference contribution
AN - SCOPUS:85108015827
T3 - Proceedings - 2021 IEEE 14th International Conference on Software Testing, Verification and Validation, ICST 2021
SP - 284
EP - 294
BT - Proceedings - 2021 IEEE 14th International Conference on Software Testing, Verification and Validation, ICST 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 April 2021 through 16 April 2021
ER -