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LIPS vs MOSA: a Replicated Empirical Study on Automated Test Case Generation
Panichella, Annibale; Kifetew, Fitsum; Tonella, Paolo
2017In International Symposium on Search Based Software Engineering (SSBSE) 2017
Peer reviewed
 

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Abstract :
[en] Replication is a fundamental pillar in the construction of scientific knowledge. Test data generation for procedural programs can be tackled using a single-target or a many-objective approach. The proponents of LIPS, a novel single-target test generator, conducted a preliminary empirical study to compare their approach with MOSA, an alternative many-objective test generator. However, their empirical investigation suffers from several external and internal validity threats, does not consider complex programs with many branches and does not include any qualitative analysis to interpret the results. In this paper, we report the results of a replication of the original study designed to address its major limitations and threats to validity. The new findings draw a completely different picture on the pros and cons of single-target vs many-objective approaches to test case generation.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Disciplines :
Computer science
Author, co-author :
Panichella, Annibale ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Kifetew, Fitsum;  Fondazione Bruno Kessler
Tonella, Paolo;  Fondazione Bruno Kessler
External co-authors :
yes
Language :
English
Title :
LIPS vs MOSA: a Replicated Empirical Study on Automated Test Case Generation
Publication date :
09 September 2017
Event name :
International Symposium on Search Based Software Engineering (SSBSE) 2017
Event place :
Paderborn, Germany
Event date :
from 09-09-2017 to 11-09-2017
Audience :
International
Main work title :
International Symposium on Search Based Software Engineering (SSBSE) 2017
Publisher :
Springer
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 14 June 2017

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