Empirical Study of Effectiveness of EvoSuite on SBST 2020 Tool Competition Benchmark (SSBSE 2021 - RENE - Replications and Negative Results)

Who

Robert Sebastian Herlim, Shin Hong, Yunho Kim, Moonzoo Kim

Track

SSBSE 2021 RENE - Replications and Negative Results

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When

Mon 11 Oct 2021 14:30 - 14:50 at SSBSE ROOM - SSBSE Session 2 Chair(s): Wesley Assunção

Abstract

EvoSuite is a state-of-the-art search-based software testing tool for Java programs and many researchers have applied EvoSuite to achieve high test coverage. However, due to the high complexity of object-oriented programs, EvoSuite still may suffer several limitations in terms of test coverage achievement. In this paper, we conducted experiments to study the limitations of EvoSuite on the most recent SBST 2020 Tool Competition benchmark that consists of 70 classes selected from real-world Java projects. We have manually classified the uncovered branches of the target programs and reported corresponding limitations of EvoSuite. Finally, we discuss those limitations and how to resolve those issues to improve EvoSuite.

Robert Sebastian Herlim

KAIST

South Korea

Shin Hong

Handong Global University

South Korea

Yunho Kim

Hanyang University

South Korea

Moonzoo Kim

KAIST and V+Lab

South Korea

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Session Program

Mon 11 Oct
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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)

14:30 20m Talk		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
14:50 30m Talk		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
15:20 20m Talk		Refining Fitness Functions for Search-Based Automated Program Repair: A Case Study with ARJA and ARJA-e Challenge 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