Traditional Mutation Operators Research Articles

Optimization of Mutation Testing Challenges to Fixing Faults

One of the challenges of mutation testing is fixing faults. In the debugging phase, all live mutants were repaired. Programs need high mutation scores to be declared reliable program codes. Each mutation test can allow the identification of multiple mutants. This is what confuses the faults fixing process. The objective of this research is to get the shortest route so that it can help in sorting the mutant types during application improvement after testing. The optimization is needed considering the number of mutants in each mutation testing. The problems related to optimization are very complex. It takes a suitable method to find the shortest path by paying attention to each point. There are 30 projects chosen randomly. The operator mutations that are often killed when testing mutations are AOIU and COI. The proposed optimization for mutant repair sequence is the ant colony system (ACS). The route selection using the Ant Colony System algorithm resulted in route optimization of 1.528254. Meanwhile, if the genetic algorithm is used, the score is 1.767643. Optimization results are very helpful for developers in improving code in mutation testing. Research states the best order for handling mutants using ACS. This research can be further developed with the addition of class-level mutant cases which are produced using class mutation operators. Class mutation operators have different characteristics from traditional mutation operators. In particular, it requires changes to the program structure, such as the definition of class variables.

An Improved Immune Algorithms for Solving Flexible Manufacturing System Distributed Production Scheduling Problem Subjects to Machine Maintenance

Competitiveness and rapid expansion of flexible manufacturing system (FMS) as one of the industrial alternatives has attracted many practitioners’ and academicians’ interest. Recent globalization events have further encouraged FMS development into distributed, self-reliant units of production center. The flexible manufacturing system in distributed system (FMSDS) considers multi-factory environments, where jobs are processed by a system of FMSs. FMSDS problems deal with the allocation of jobs to factories, independent assignment of job operation to the machines, and operations sequencing on the machine. Additionally, in many previous studies, impact of maintenance as one of the core parts of production scheduling has been neglected. This significantly affects the overall performance of the production scheduling. As such, maintenance has been considered in this paper as part of the production scheduling. The objective of this paper is to minimize the global makespan over all the factories. This paper proposes an Improved Immune Algorithm (IIA) to solve the FMSDS problem. Antibody encoding adoption explicitly represents the information of factory, job, and maintenance, whilst a greedy decoding procedure exploits flexibility and determines the job routing. Rather than s traditional mutation operator, an improvised mutation operator is used to improve the solutions by refining the most promising individuals of each generation. The proposed approach has been compared with other algorithms and obtained satisfactory results, where the algorithm performance has been tested with several parameter tunings.

Investigating Different Metrics for Evaluation and Selection of Mutation Operators for Java

Mutation testing is a successful and powerful technique, specifically designed for injecting the artificial faults. Although it is effective at revealing the faults, test suite assessment and its reduction, however, suffer from the expense of executing a large number of mutants. The researchers have proposed different types of cost reduction techniques in the literature. These techniques highly depend on the inspection of mutation operators. Several metrics have been evolved for the same. The selective mutation technique is most frequently used by the researchers. In this paper, the authors investigate different metrics for evaluating the traditional mutation operators for Java. Results on 13 Java programs indicate how grouping few operators can impact the effectiveness of an adequate and minimal test suite, and how this could provide several cost benefits.

Ranking-based hierarchical random mutation in differential evolution.

In order to improve the performance of differential evolution (DE), this paper proposes a ranking-based hierarchical random mutation in differential evolution (abbreviated as RHRMDE), in which two improvements are presented. First, RHRMDE introduces a hierarchical random mutation mechanism to apply the classic “DE/rand/1” and its variant on the non-inferior and inferior group determined by the fitness value. The non-inferior group employs the traditional mutation operator “DE/rand/1” with global and random characteristics, which increases the global exploration ability and population diversity. The inferior group uses the improved mutation operator “DE/rand/1” with elite and random characteristics, which enhances the local exploitation ability and convergence speed. Second, the control parameter adaptation of RHRMDE not only considers the complexity differences of various problems but also takes individual differences into account. The proposed RHRMDE is compared with five DE variants and five non-DE algorithms on 32 universal benchmark functions, and the results show that the RHRMDE is superior over the compared algorithms.

Differential Evolution for Multimodal Optimization With Species by Nearest-Better Clustering.

Multimodal optimization problems (MMOPs) are common in real-world applications and involve identifying multiple optimal solutions for decision makers to choose from. The core requirement for dealing with such problems is to balance the ability of exploration in the global space and exploitation in the multiple optimal areas. In this paper, based on the differential evolution (DE), we propose a novel algorithm focusing on the formulation, balance, and keypoint of species for MMOPs, called FBK-DE. First, nearest-better clustering (NBC) is used to divide the population into multiple species with minimum size limitations. Second, to avoid placing too many individuals into one species, a species balance strategy is proposed to adjust the size of each species. Third, two keypoint-based mutation operators named DE/keypoint/1 and DE/keypoint/2 are proposed to evolve each species together with traditional mutation operators. The experimental results of FBK-DE on 20 benchmark functions are compared with 15 state-of-the-art multimodal optimization algorithms. The comparisons show that the proposed FBK-DE performs competitively with these algorithms.

A Bagging Based Multiobjective Differential Evolution With Multiple Subpopulations

Different from multiobjective differential evolution algorithm (MODE) based on traditional mutation operators and a single population, this paper developed a bagging based MODE with multiple subpopulations (BagMPMODE) by incorporating the idea of bagging into the evolution process of MODE. In this algorithm, multiple subpopulations with different evolution operators are adopted to maintain search diversity, as did by some previous researches on MODE. During evolution, the subpopulations will compete with each other, i.e., the size of each subpopulation will be adjusted based on its contribution to the whole search result. Based on the multiple subpopulation strategy, the idea of bagging ensemble is adopted to generate offspring solutions, which can be viewed as the cooperation of these multiple subpopulations. The proposed BagMPMODE algorithm is evaluated on a set of 22 benchmark problems, and computational experiments illustrate that the BagMPMODE algorithm is competitive or even superior to several state-of-the-art MODEs and some other multiobjective evolutionary algorithms in the literature for most problems.

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem.

It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.

Artificial immune network clustering based on a cultural algorithm

Data mining technology has been applied in many fields. Prototype-based cluster analysis is an important data mining method, but its ability to discover knowledge is limited because of the need to know the number of target data categories and cluster prototypes in advance. Artificial immune evolutionary network clustering is a clustering method based on network structure. Compared with prototype-based cluster analysis, it has the advantage of realizing unsupervised learning and clustering without any prior knowledge of data. However, artificial immune evolutionary network clustering also has problems such as a lack of guidance in the clustering process, fuzzy boundary sensitivity, and difficulty in determining parameters. To solve these problems, an artificial immune network clustering algorithm based on a cultural algorithm is proposed. First, three kinds of knowledge are constructed: normative knowledge is used to regulate the spatial range of population initialization to avoid blindness; state knowledge is used to distinguish the type of antigen, and immune defense measures are taken to prevent the network structure caused by noise and boundaries from being unclear; and topology knowledge is used to guide the antigen for optimal antibody search. Second, topology knowledge in the cultural algorithm is used to characterize the distribution of antigens and antibodies in space, and elite learning is used to improve the traditional clone mutation operator. Based on the shadow set theory, a method for adaptively determining the compression threshold is proposed. Finally, the results of simulation experiments show that the proposed algorithm can effectively overcome the above problems, and the clustering performances on a synthetic dataset and an actual dataset are satisfactory.

Performance mutation testing

SummaryPerformance bugs are known to be a major threat to the success of software products. Performance tests aim to detect performance bugs by executing the program through test cases and checking whether it exhibits a noticeable performance degradation. The principles of mutation testing, a well‐established testing technique for the assessment of test suites through the injection of artificial faults, could be exploited to evaluate and improve the detection power of performance tests. However, the application of mutation testing to assess performance tests, henceforth called performance mutation testing (PMT), is a novel research topic with numerous open challenges. In previous papers, we identified some key challenges related to PMT. In this work, we go a step further and explore the feasibility of applying PMT at the source‐code level in general‐purpose languages. To do so, we revisit concepts associated with classical mutation testing and design seven novel mutation operators to model known bug‐inducing patterns. As a proof of concept, we applied traditional mutation operators as well as performance mutation operators to open‐source C++ programs. The results reveal the potential of the new performance‐mutants to help assess and enhance performance tests when compared with traditional mutants. A review of live mutants in these programs suggests that they can induce the design of special test inputs. In addition to these promising results, our work brings a whole new set of challenges related to PMT, which will hopefully serve as a starting point for new contributions in the area.

Differential evolution with k-nearest-neighbour-based mutation operator

Differential evolution (DE) is one of the most powerful global numerical optimisation algorithms in the evolutionary algorithm family and it is popular for its simplicity and effectiveness in solving numerous real-world optimisation problems in real-valued spaces. The performance of DE depends on its mutation strategy. However, the traditional mutation operators are difficult to balance the exploration and exploitation. To address these issues, in this paper, a k-nearest-neighbour-based mutation operator is proposed for improving the search ability of DE. The k-nearest-neighbour-based mutation operator is used to search in the areas which the vector density distribution is sparse. This method enhances the exploitation of DE and accelerates the convergence of the algorithm. In order to evaluate the effectiveness of our proposed mutation operator on DE, this paper compares other state-of-the-art evolutionary algorithms with the proposed algorithm. Experimental verifications are conducted on the CEC '05 competition and two real-world problems. Experimental results indicate that our proposed mutation operator is able to enhance the performance of DE and can perform significantly better than, or at least comparable to, several state-of-the-art DE variants.

A Memetic Algorithm for Community Detection in Bipartite Networks

Bipartite networks belong to the category of complex networks, whose vertices can be divided into two separated vertex sets, so that there are no edges between vertices in the same set, and edges only exist between vertices in different sets. In the past decades, although community discovery in one-mode networks has been deeply explored, the detection of communities in bipartite networks has not been widely studied. In this paper, we present a new memetic algorithm named MATMCD-BN for community detection in bipartite networks with two types of nodes in the community. Firstly, we put forward a new initialization method for population initialization of memetic algorithm for bipartite network communities discovery, which can expedite the convergence speed of this algorithm. Secondly, besides using traditional mutation operator, we propose a new crossover operator (called two-way random crossover operator in this paper) and a new mutation operator (called mutation operator 2 in this paper), which are helpful to improve the accuracy of the solution and accelerate the convergence speed of the proposed algorithm. Finally, we develop a local search method, which can make the solution approach the global optimal solution quickly and jump out of the local optimal solution with a certain probability. As far as we know, the proposed MATMCD-BN is the first memetic algorithm (MA) applied to community detection in bipartite networks with two types of nodes in the community. In order to confirm the performance of this algorithm, we have done a lot of experiments using synthetic and real social networks. The experimental results demonstrate that the presented method is effective and promising for bipartite network community identification.

Genetic Programming varvec{+} Proof Search varvec{=} Automatic Improvement

Search Based Software Engineering techniques are emerging as important tools for software maintenance. Foremost among these is Genetic Improvement, which has historically applied the stochastic techniques of Genetic Programming to optimize pre-existing program code. Previous work in this area has not generally preserved program semantics and this article describes an alternative to the traditional mutation operators used, employing deterministic proof search in the sequent calculus to yield semantics-preserving transformations on algebraic data types. Two case studies are described, both of which are applicable to the recently-introduced ‘grow and graft’ technique of Genetic Improvement: the first extends the expressiveness of the ‘grafting’ phase and the second transforms the representation of a list data type to yield an asymptotic efficiency improvement.

Assessment of class mutation operators for C[formula omitted] with the MuCPP mutation system

Context: Mutation testing has been mainly analyzed regarding traditional mutation operators involving structured programming constructs common in mainstream languages, but mutations at the class level have not been assessed to the same extent. This fact is noteworthy in the case of C++, despite being one of the most relevant languages including object-oriented features. Objective: This paper provides a complete evaluation of class operators for the C++ programming language. MuCPP, a new system devoted to the application of mutation testing to this language, was developed to this end. This mutation system implements class mutation operators in a robust way, dealing with the inherent complexity of the language. Method:MuCPP generates the mutants by traversing the abstract syntax tree of each translation unit with the Clang API, and stores mutants as branches in the Git version control system. The tool is able to detect duplicate mutants, avoid system headers, and drive the compilation process. Then, MuCPP is used to conduct experiments with several open-source C++ programs. Results: The improvement rules listed in this paper to reduce unproductive class mutants have a significant impact in the computational cost of the technique. We also calculate the quantity and distribution of mutants generated with class operators, which generate far fewer mutants than their traditional counterparts. Conclusions: We show that the tests accompanying these programs cannot detect faults related to particular object-oriented features of C++. In order to increase the mutation score, we create new test scenarios to kill the surviving class mutants for all the applications. The results confirm that, while traditional mutation operators are still needed, class operators can complement them and help testers further improve the test suite.

Memory mutation testing

ContextThree decades of mutation testing development have given software testers a rich set of mutation operators, yet relatively few operators can target memory faults (as we demonstrate in this paper).ObjectiveTo address this shortcoming, we introduce Memory Mutation Testing, proposing 9 Memory Mutation Operators each of which targets common forms of memory fault. We compare Memory Mutation Operators with traditional Mutation Operators, while handling equivalent and duplicate mutants.MethodWe extend our previous workshop paper, which introduced Memory Mutation Testing, with a more extensive and precise analysis of 18 open source programs, including 2 large real-world programs, all of which come with well-designed unit test suites. Specifically, our empirical study makes use of recent results on Trivial Compiler Equivalence (TCE) to identify both equivalent and duplicate mutants. Though the literature on mutation testing has previously deployed various techniques to cater for equivalent mutants, no previous study has catered for duplicate mutants.ResultsCatering for such extraneous mutants improves the precision with which claims about mutation scores can be interpreted. We also report the results of a new empirical study that compares Memory Mutation Testing with traditional Mutation Testing, providing evidence to support the claim that traditional mutation testing inadequately captures memory faults; 2% of the memory mutants are TCE-duplicates of traditional mutants and average test suite effectiveness drops by 44% when the target shifts from traditional mutants to memory mutants.ConclusionsIntroducing Memory Mutation Operators will cost only a small portion of the overall testing effort, yet generate higher quality mutants compared with traditional operators. Moreover, TCE technique does not only help with reducing testing effort, but also improves the precision of assessment on test quality, therefore should be considered in other Mutation Testing studies.

A DE-Based Scatter Search for Global Optimization Problems

This paper proposes a hybrid scatter search (SS) algorithm for continuous global optimization problems by incorporating the evolution mechanism of differential evolution (DE) into the reference set updated procedure of SS to act as the new solution generation method. This hybrid algorithm is called a DE-based SS (SSDE) algorithm. Since different kinds of mutation operators of DE have been proposed in the literature and they have shown different search abilities for different kinds of problems, four traditional mutation operators are adopted in the hybrid SSDE algorithm. To adaptively select the mutation operator that is most appropriate to the current problem, an adaptive mechanism for the candidate mutation operators is developed. In addition, to enhance the exploration ability of SSDE, a reinitialization method is adopted to create a new population and subsequently construct a new reference set whenever the search process of SSDE is trapped in local optimum. Computational experiments on benchmark problems show that the proposed SSDE is competitive or superior to some state-of-the-art algorithms in the literature.

An improved NSGA-II algorithm for multi-objective lot-streaming flow shop scheduling problem

Crossover and mutation operators in NSGA-II are random and aimless, and encounter difficulties in generating offspring with high quality. Aiming to overcoming these drawbacks, we proposed an improved NSGA-II algorithm (INSGA-II) and applied it to solve the lot-streaming flow shop scheduling problem with four criteria. We first presented four variants of NEH heuristic to generate the initial population, and then incorporated the estimation of distribution algorithm and a mutation operator based on insertion and swap into NSGA-II to replace traditional crossover and mutation operators. Last but not least, we performed a simple and efficient restarting strategy on the population when the diversity of the population is smaller than a given threshold. We conducted a serial of experiments, and the experimental results demonstrate that the proposed algorithm outperforms the comparative algorithms.

A logic mutation approach to selective mutation for programs and queries

Program mutation testing is a technique for measuring and generating high quality test data. However, traditional mutation operators are not necessarily efficient or effective. We address three specific issues. One, test data that kills all mutants generated by current mutation tools can still miss detection of some common logic faults because such tools lack appropriate logic mutation operators. Two, the number of mutants generated is often unnecessarily large. Three, many equivalent mutants can be generated and these can be difficult to eliminate. This paper explores the idea of addressing these issues by selectively generating only specially engineered subsuming higher order logic mutants. However, such an approach is only useful if a test set that kills all such mutants also kills a high percentage of general mutants. An empirical study was conducted using a tool that generates only subsuming higher order logic mutants and tools that generate general mutants. Both Java code and SQL were used as the source under test. For both the software and queries, tests killing all the subsuming higher order mutants killed a high percentage of general mutants while reducing both the number of mutants and the number of equivalent mutants. The conclusion is that, for the test subjects studied, subsuming higher order logic mutation is an effective approach to selective mutation for programs and queries.

Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System

In this paper, we apply an immune-inspired approach to design ensembles of heterogeneous neural networks for classification problems. Our proposal, called Bayesian artificial immune system, is an estimation of distribution algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network, representing the joint distribution of promising solutions. Among the additional attributes provided by the Bayesian framework inserted into an immune-inspired search algorithm are the automatic control of the population size along the search and the inherent ability to promote and preserve diversity among the candidate solutions. Both are attributes generally absent from alternative estimation of distribution algorithms, and both were shown to be useful attributes when implementing the generation and selection of components of the ensemble, thus leading to high-performance classifiers. Several aspects of the design are illustrated in practical applications, including a comparative analysis with other attempts to synthesize ensembles.

Multi‐objective feature selection using a Bayesian artificial immune system

PurposeThe purpose of this paper is to apply a multi‐objective Bayesian artificial immune system (MOBAIS) to feature selection in classification problems aiming at minimizing both the classification error and cardinality of the subset of features. The algorithm is able to perform a multimodal search maintaining population diversity and controlling automatically the population size according to the problem. In addition, it is capable of identifying and preserving building blocks (partial components of the whole solution) effectively.Design/methodology/approachThe algorithm evolves candidate subsets of features by replacing the traditional mutation operator in immune‐inspired algorithms with a probabilistic model which represents the probability distribution of the promising solutions found so far. Then, the probabilistic model is used to generate new individuals. A Bayesian network is adopted as the probabilistic model due to its capability of capturing expressive interactions among the variables of the problem. In order to evaluate the proposal, it was applied to ten datasets and the results compared with those generated by state‐of‐the‐art algorithms.FindingsThe experiments demonstrate the effectiveness of the multi‐objective approach to feature selection. The algorithm found parsimonious subsets of features and the classifiers produced a significant improvement in the accuracy. In addition, the maintenance of building blocks avoids the disruption of partial solutions, leading to a quick convergence.Originality/valueThe originality of this paper relies on the proposal of a novel algorithm to multi‐objective feature selection.

A genetic local search algorithm for minimizing total flowtime in the permutation flowshop scheduling problem

Recently, the flowshop scheduling problem to minimize total flowtime has attracted more attention from researchers. In this paper, a genetic local search algorithm is proposed to solve this problem. The proposed algorithm hybridizes the genetic algorithm and the tabu search. It employs the genetic algorithm to do the global search and the tabu search to do the local search. The orthogonal-array-based crossover is utilized to enhance the capability of intensification. Also, a novel orthogonal-array-based mutation is proposed, in order to add capability of intensification to the traditional mutation operator. The performance of the proposed genetic local search algorithm is very competitive. It improved 54 out of 90 current best solutions reported in the literature for short-term search, and it also improved 18 out of 20 current best solutions reported in the literature for long-term search.