Write a Blog >>
SSBSE 2021
Mon 11 - Tue 12 October 2021
co-located with ESEM 2021
Mon 11 Oct 2021 15:20 - 15:40 at SSBSE ROOM - SSBSE Session 2 Chair(s): Wesley Assunção

Automated Program Repair (APR) strives to automatically fix faulty software without human-intervention. Search-based APR iteratively generates possible patches for a given buggy software, guided by the execution of the patched program on a given test suite (i.e., a set of test cases). Search-based approaches have generally only used Boolean test case results (i.e., pass or fail), but recently more fined-grained fitness evaluations have been investigated with promising yet unsettled results. Using the most recent extension of the very popular Defects4J bug dataset, we conduct an empirical study using ARJA and ARJA-e, two state-of-the-art search-based APR systems using a Boolean and a non-Boolean fitness function, respectively. We aim to both extend previous results using new bugs from Defects4J v2.0 and to settle whether refining the fitness function helps fixing bugs present in large software.

In our experiments using 151 non-deprecated and not previously evaluated bugs from Defects4J v2.0, ARJA was able to find patches for 6.62% (10/151) of bugs, whereas ARJA-e found patches for 7.24% (12/151) of bugs. We thus observe only small advantage to using the refined fitness function. This contrasts with the previous work using Defects4J v1.0.1 where ARJA was able to find adequate patches for 24.2% (59/244) of the bugs and ARJA-e for 43.4% (106/244). These results may indicate a potential overfitting of the tools towards the previous version of the Defects4J dataset.

Presentation at: https://youtu.be/lcpYTv1TaE8

Mon 11 Oct

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

14:30 - 15:40
SSBSE Session 2Challenge / Research Papers / RENE - Replications and Negative Results at SSBSE ROOM
Chair(s): Wesley Assunção Pontifical Catholic University of Rio de Janeiro (PUC-Rio)
Empirical Study of Effectiveness of EvoSuite on SBST 2020 Tool Competition Benchmark
RENE - Replications and Negative Results
Robert Sebastian Herlim KAIST, Shin Hong Handong Global University, Yunho Kim Hanyang University, Moonzoo Kim KAIST and V+Lab
Multi-objective Test Case Selection Through Linkage Learning-driven Crossover
Research Papers
Mitchell Olsthoorn Delft University of Technology, Annibale Panichella Delft University of Technology
Link to publication DOI Pre-print
Refining Fitness Functions for Search-Based Automated Program Repair: A Case Study with ARJA and ARJA-e
Giovani Guizzo University College London, Aymeric Blot University College London, James Callan UCL, Justyna Petke University College London, Federica Sarro University College London
Link to publication Pre-print