An Approach for the Generation of Higher Order Mutants Using Genetic Algorithms

Abuljadayel, Anas; Wedyan, Fadi

doi:10.5815/ijisa.2018.01.05

Cited by 16 publications

(5 citation statements)

References 45 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Abuljadayel and Wedyan [39] proposed an approach and a tool for finding subtle HOMs by using a genetic algorithm. Their approach used an enhanced mode of crossover called crossover by replacement, wherein faults from two parent mutants are replaced to produce two new mutants that have some properties of their parents.…”

Section: Related Workmentioning

confidence: 99%

GaSubtle: A New Genetic Algorithm for Generating Subtle Higher-Order Mutants

2022

Self Cite

View full text Add to dashboard Cite

Mutation testing is an effective, yet costly, testing approach, as it requires generating and running large numbers of faulty programs, called mutants. Mutation testing also suffers from a fundamental problem, which is having a large percentage of equivalent mutants. These are mutants that produce the same output as the original program, and therefore, cannot be detected. Higher-order mutation is a promising approach that can produce hard-to-detect faulty programs called subtle mutants, with a low percentage of equivalent mutants. Subtle higher-order mutants contribute a small set of the large space of mutants which grows even larger as the order of mutation becomes higher. In this paper, we developed a genetic algorithm for finding subtle higher-order mutants. The proposed approach uses a new mechanism in the crossover phase and uses five selection techniques to select mutants that go to the next generation in the genetic algorithm. We implemented a tool, called GaSubtle that automates the process of creating subtle mutants. We evaluated the proposed approach by using 10 subject programs. Our evaluation shows that the proposed crossover generates more subtle mutants than the technique used in a previous genetic algorithm with less execution time. Results vary on the selection strategies, suggesting a dependency relation with the tested code.

show abstract

Section: Related Workmentioning

confidence: 99%

GaSubtle: A New Genetic Algorithm for Generating Subtle Higher-Order Mutants

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…HOMT is another kind of mutation testing introduced by Jia et al [15, 16]. As efficiency is known to be a bugbear of mutation testing in general, HOMT aims to improve the efficiency of mutation testing by combining FOMs into a single HOM [7, 19–22, 24, 40–43]. HOMT is considered one of the most promising solutions to alleviate the high‐cost problem of mutation testing by reducing the number of mutants [44, 45].…”

Section: Related Workmentioning

confidence: 99%

“…There exist mainly two kinds of HOM generation strategies. The first one is to use search‐based algorithms to explore the vast search space to acquire HOMs that are harder to kill and more realistic than the constituent FOMs [14–16, 40, 41, 45–47]. Such HOMs are then used to assess and improve the quality of test suites.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to reducing the number of mutants, another advantage of HOMT is that it generates significantly fewer equivalent mutants than the first‐order mutation [17–20, 24, 40, 42, 44, 52, 53]. Jia and Harman [27] and Madeyski et al [24] argue that the difficulty of identifying equivalent mutants is the primary reason why mutation is not used more widely.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

HOTFUZ: Cost‐effective higher‐order mutation‐based fault localization

Jang

Ryu

Baik

2021

Software Testing Verif & Rel

View full text Add to dashboard Cite

Fault localization techniques are used to deduce the exact source of a failure from a set of failure indications while debugging software and play a crucial role in improving software quality. Mutation‐based fault localization (MBFL) techniques are proposed to localize faults at a finer granularity and with higher accuracy than traditional fault localization techniques. Despite the technique's effectiveness, the immense cost of mutation analysis hinders MBFL's practical application in the industry. Various mutation alternative strategies are utilized to lower the cost of MBFL, but they sacrifice the accuracy of localization results. Higher‐order mutation testing was proposed to search for valuable mutants that drive testing harder and reduce the overall test effort. However, higher‐order mutants (HOMs) never have been used to address the cost problem of MBFL to the extent of our knowledge. This paper proposes a novel, cost‐effective MBFL technique called HOTFUZ, Higher‐Order muTation‐based FaUlt localiZation, that employs HOMs to reduce the cost while minimizing the accuracy degradation. HOTFUZ combines mutants of a program under test into HOMs to decrease the number of mutants by more than half, depending on the order of HOMs. An experimental study is conducted using 65 real‐world faults of CoREBench to assess the proposed approach's cost‐effectiveness. The experimental results show that HOTFUZ outperforms the extant mutation alternative strategies by localizing faults more accurately using the same number of mutants executed. HOTFUZ has three main benefits over existing mutant reduction techniques for MBFL: (a) It keeps the advantage of using the whole set of mutation operators; (b) it does not discard generated mutants randomly for the sake of efficiency; and, finally, (c) it significantly decreases the proportion of equivalent mutants.

show abstract

“…Langdon, Harman and Jia [20] built higher order mutants that are harder to kill than any first-order mutant. More recently, Abuljadayel and Wedyan [21] presented an approach to generate higher order mutants using a genetic algorithm, also harder to kill than first order mutants. 2 Mutant Sampling is a simple approach that randomly chooses a small subset of mutants from the entire set, according to a predefined percentage.…”

Section: Cost Reduction Techniquesmentioning

confidence: 99%

Analysing the combination of cost reduction techniques in Android mutation testing

Usaola

Rodríguez-Trujillo

2021

Software Testing Verif & Rel

View full text Add to dashboard Cite

When applied to mobile software, mutation testing is particularly costly due to the deployment of the app under test onto the device: if one deployment is made for each generated mutant, the execution time becomes unapproachable. This paper analyses how the combination of different cost reduction techniques improves the execution time of mutation testing in mobile apps. The techniques reviewed and combined are mutant schema, parallel execution and two different ways of executing tests against the mutants (all against all and all against mutants remaining alive), as well as greedy algorithm for reducing the test suite size. This paper also presents a mathematical model of cost reduction and checks its validity with several experiments. Furthermore, the exhaustive and long experimentation has led the authors to compile a set of good practices which are also presented in a set of lessons learned.

show abstract

An Approach for the Generation of Higher Order Mutants Using Genetic Algorithms

Cited by 16 publications

References 45 publications

GaSubtle: A New Genetic Algorithm for Generating Subtle Higher-Order Mutants

GaSubtle: A New Genetic Algorithm for Generating Subtle Higher-Order Mutants

HOTFUZ: Cost‐effective higher‐order mutation‐based fault localization

Analysing the combination of cost reduction techniques in Android mutation testing

Contact Info

Product

Resources

About