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SSBSE 2021
Mon 11 - Tue 12 October 2021
co-located with ESEM 2021
Mon 11 Oct 2021 14:50 - 15:20 at SSBSE ROOM - SSBSE Session 2 Chair(s): Wesley Assunção

Test case selection (TCS) aims to select a subset of the test suite to run for regression testing. The selection is typically based on past coverage and execution cost data. Researchers have successfully used multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and its variants, to solve the problem. These MOEAs use traditional crossovers to create new candidate solutions during the search. Recent studies in evolutionary computation showed that more effective recombinations can be made by using linkage learning. Inspired by these recent advances in this field, we propose a new variant of NSGA-II, called L2-NSGA, that uses linkage learning to optimize test case selection. In particular, we use an unsupervised clustering algorithm to infer promising patterns among the solutions (sub-test suites). Then, these patterns are used in the next iterations of L2-NSGA to create solutions that contain/preserve these inferred patterns. Our results show that our customizations make NSGA-II more effective for test case selection. Furthermore, the test suite sub-sets generated by L2-NSGA are less expensive and more effective (detect more faults) than those generated by MOEAs used in the literature for regression testing.

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