Constructing subtle higher order mutants for Java and AspectJ programs
Elmahdi Omar,Sudipto Ghosh,Darrell Whitley +2 more
- 01 Nov 2013
- pp 340-349
TL;DR: Three search-based algorithms are introduced (Genetic Algo-rithm, Local Search, and Random Search) for finding subtle HOMs in Java and AspectJ programs that are not killed by an existing test set that kills all the first order mutants of a given program.
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Abstract: One goal of higher order mutation testing is to produce higher order mutants (HOMs) that represent subtle faults. We define subtle HOMs as those that are not killed by an existing test set that kills all the first order mutants of a given program. The fault detection effectiveness of the test set can be improved by adding test cases that kill subtle HOMs. However, finding subtle HOMs can be costly even for small programs because of the large space of candidate HOMs. Moreover, a large majority of HOMs are killed by test sets that kill all first order mutants, making the subtle ones relatively rare. We introduce three search-based algorithms (Genetic Algo-rithm, Local Search, and Random Search) for finding subtle HOMs in Java and AspectJ programs. All three algorithms found subtle HOMs for all studied programs but Local Search was more successful in finding subtle HOMs than Genetic Algorithm and Random Search.
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Figures
Figure 8.10: Number of subtle HOMs with respect to the number of search techniques that found them for Banking 
Figure 9.21: Distribution of HOMs based on the location of their constituent FOMs for Cruise Control (AspectJ) 
Table 7.11: Number of subtle HOMs that were found for Coordinate 
Figure 8.18: Number of subtle HOMs with respect to the number of search techniques that found them for Roman 
Figure 9.1: Distribution of Subtle HOMs based on their Mutation Operators for Cruise Control (Java) 
Table 11.2: Comparing the number of subtle HOMs that were found by the search techniques and by composing subtle HOMs that were found by Restricted Enumeration Search
Citations
Mutation Testing Advances: An Analysis and Survey
Mike Papadakis,Marinos Kintis,Jie Zhang,Yue Jia,Yves Le Traon,Mark Harman +5 more
- 01 Jan 2019
TL;DR: This chapter presents a survey of recent advances, over the past decade, related to the fundamental problems of mutation testing and sets out the challenges and open problems for the future development of the method.
Angels and monsters: an empirical investigation of potential test effectiveness and efficiency improvement from strongly subsuming higher order mutation
Mark Harman,Yue Jia,Pedro Reales Mateo,Macario Polo +3 more
- 15 Sep 2014
TL;DR: Using SSHOMs in place of the first order mutants they subsume yielded a 35%-45% reduction in the number of mutants required, while simultaneously improving test efficiency by 15% and effectiveness by between 5.6% and 12%.
49
An Approach for the Generation of Higher Order Mutants Using Genetic Algorithms
Anas Abuljadayel,Fadi Wedyan +1 more
TL;DR: The approach was able to produce subtle higher order mutants, the fitness of mutants improved by almost 99% compared with the first order mutants used in the experiment, and the percentage of produced equivalent mutants was about 4%.
Comparing search techniques for finding subtle higher order mutants
Elmahdi Omar,Sudipto Ghosh,Darrell Whitley +2 more
- 12 Jul 2014
TL;DR: This study shows that more subtle HOMs were found when the new heuristics and search strategies were used, and the programming language (Java or AspectJ) did not affect the effectiveness of any search technique.
27
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TL;DR: The paper introduces the concept of a subsuming HOM; one that is harder to kill than the first order mutants from which it is constructed, by definition, subsumed HOMs denote subtle fault combinations.
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