TY - GEN
T1 - Learning fault localisation for both humans and machines using multi-objective GP
AU - Choi, Kabdo
AU - Sohn, Jeongju
AU - Yoo, Shin
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Genetic Programming has been successfully applied to fault localisation to learn ranking models that place the faulty program element as near the top as possible. However, it is also known that, when localisation results are used by Automatic Program Repair (APR) techniques, higher rankings of faulty program elements do not necessarily result in better repair effectiveness. Since APR techniques tend to use localisation scores as weights for program mutation, lower scores for non-faulty program elements are as important as high scores for faulty program elements. We formulate a multi-objective version of GP based fault localisation to learn ranking models that not only aim to place the faulty program element higher in the ranking, but also aim to assign as low scores as possible to non-faulty program elements. The results show minor improvements in the suspiciousness score distribution. However, surprisingly, the multi-objective formulation also results in more accurate fault localisation ranking-wise, placing 155 out of 386 faulty methods at the top, compared to 135 placed at the top by the single objective formulation.
AB - Genetic Programming has been successfully applied to fault localisation to learn ranking models that place the faulty program element as near the top as possible. However, it is also known that, when localisation results are used by Automatic Program Repair (APR) techniques, higher rankings of faulty program elements do not necessarily result in better repair effectiveness. Since APR techniques tend to use localisation scores as weights for program mutation, lower scores for non-faulty program elements are as important as high scores for faulty program elements. We formulate a multi-objective version of GP based fault localisation to learn ranking models that not only aim to place the faulty program element higher in the ranking, but also aim to assign as low scores as possible to non-faulty program elements. The results show minor improvements in the suspiciousness score distribution. However, surprisingly, the multi-objective formulation also results in more accurate fault localisation ranking-wise, placing 155 out of 386 faulty methods at the top, compared to 135 placed at the top by the single objective formulation.
KW - Fault localisation
KW - Multi-objective evolutionary algorithm
UR - http://www.scopus.com/inward/record.url?scp=85053117599&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-99241-9_20
DO - 10.1007/978-3-319-99241-9_20
M3 - Conference contribution
AN - SCOPUS:85053117599
SN - 9783319992402
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 349
EP - 355
BT - Search-Based Software Engineering - 10th International Symposium, SSBSE 2018, Proceedings
A2 - McMinn, Phil
A2 - Colanzi, Thelma Elita
PB - Springer Verlag
T2 - 10th International Symposium on Search-Based Software Engineering, SSBSE 2018
Y2 - 8 September 2018 through 10 September 2018
ER -