
Title
GenMorph: Automatically Generating Metamorphic Relations via Genetic ProgrammingOther institutions
University of AucklandKing's College London
Università della Svizzera italiana (USI) (Suiza)
Version
Postprint
Rights
© 2024 IEEEAccess
Open accessPublisher’s version
https://doi.org/10.1109/TSE.2024.3407840Published at
IEEE Transactions on Software Engineering Publisher
IEEEKeywords
Metamorphic Testing
oracle improvement
genetic programming
mutation analysis ... [+]
oracle improvement
genetic programming
mutation analysis ... [+]
Metamorphic Testing
oracle improvement
genetic programming
mutation analysis
ODS 9 Industria, innovación e infraestructura [-]
oracle improvement
genetic programming
mutation analysis
ODS 9 Industria, innovación e infraestructura [-]
Abstract
Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold a ... [+]
Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold among multiple test inputs and corresponding outputs. Deriving MRs is mostly a manual activity, since their automated generation is a challenging and largely unexplored problem. This paper presents GenMorph , a technique to automatically generate MRs for Java methods that involve inputs and outputs that are boolean, numerical, or ordered sequences. GenMorph uses an evolutionary algorithm to search for effective test oracles, i.e., oracles that trigger no false alarms and expose software faults in the method under test. The proposed search algorithm is guided by two fitness functions that measure the number of false alarms and the number of missed faults for the generated MRs. Our results show that GenMorph generates effective MRs for 18 out of 23 methods (mutation score >20%). Furthermore, it can increase Randoop ’s fault detection capability in 7 out of 23 methods, and Evosuite ’s in 14 out of 23 methods. When compared with AUTOMR, a state-of-the-art MR generator, GenMorph also outperformed its fault detection capability in 9 out of 10 methods. [-]
Funder
Gobierno VascoGobierno Vasco
Program
Elkartek 2022Ikertalde Convocatoria 2022-2023
Number
KK-2022/00119IT1519-22
Award URI
Sin informaciónSin información
Project
Edge Technologies for Industrial Distributed AI Applications (EGIA)Ingeniería de Software y Sistemas
Collections
- Articles - Engineering [700]