3979097
doi
10.5281/zenodo.3979097
oai:zenodo.org:3979097
user-empirical-software-engineering
user-eu
Devroey, Xavier
Delft University of Technology
Zaidman, Andy
Delft University of Technology
van Deursen, Arie
Delft University of Technology
Panichella, Annibale
Delft University of Technology
Replication package of "Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper Objectives"
Derakhshanfar, Pouria
Delft University of Technology
url:https://github.com/STAMP-project/Botsing-multi-objectivization-using-helper-objectives-application/tree/1.0
info:eu-repo/semantics/openAccess
Other (Open)
crash reproduction
search-based software testing
multi-objective evolutionary algorithms
<p>The replication package for the study about using new helper objectives (MOHO) for crash reproduction. This study has been accepted at ASE 2020.</p>
<p> </p>
<p>Abstract:</p>
<p>Evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called <em>Crash Distance</em> to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called <em>MO-HO</em>, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-Objectives for crash reproduction, namely <em>test length</em> (to minimize) and <em>method sequence diversity</em> (to maximize), in addition to <em>Crash Distance</em>.</p>
<p>We assessed <em>MO-HO</em> using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 hard-to-reproduce crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for <em>MO-HO</em>.</p>
<p>We evaluated this best-performing algorithm for <em>MO-HO</em> against the state-of-the-art: single-objective approach (Single-Objective Search) and decomposition-based multi-objectivization approach (<em>De-MO</em>). Our results show that <em>MO-HO</em> reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, <em>MO-HO</em> improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-Objective Search and <em>De-MO</em>, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time. </p>
Zenodo
2020-08-11
info:eu-repo/semantics/other
3979096
user-empirical-software-engineering
user-eu
1.0
award_title=Software Testing AMPlification; award_number=731529; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/731529; funder_id=00k4n6c32; funder_name=European Commission;
1597156879.480409
8070141492
md5:8b13928c738cabe57b5992ca6d8defc6
https://zenodo.org/records/3979097/files/STAMP-project/Botsing-multi-objectivization-using-helper-objectives-application-1.0.zip
public
https://github.com/STAMP-project/Botsing-multi-objectivization-using-helper-objectives-application/tree/1.0
Is supplement to
url
10.5281/zenodo.3979096
isVersionOf
doi