Mutation testing in UTP

Fault in a software program can be detected by mutation testing. However, mutation testing is an expensive process in a software testing domain. In this paper, we have introduced a method based on genetic algorithm and mutation analysis for unit testing process. Software industry produces high quality software in which software testing has an important role. First, we make a program/software and intent some mutant in this program/software, find most critical path and optimise test cases using genetic algorithm for the unit testing. Initially generated test cases are refined using genetic algorithm. We use a mutant function for measuring the adequacy of the test case set. The given mutant function is used to calculate a mutant score. We have achieved 100% path coverage and boundary coverage using mutation testing. The objective is to produce a set of good test cases for killing one or more undesired mutants and produces different mutant from original software/program. Unlike simple algorithms, genetic algorithms provide suitability for reducing the data generation at a comparable cost. An optimised test case has been generated by proposed approach for cost reduction and revealing or killing undesired test cases.

Fault in a software program can be detected by mutation testing. However, mutation testing is an expensive process in a software testing domain. In this paper, we have introduced a method based on genetic algorithm and mutation analysis for unit testing process. Software industry produces high quality software in which software testing has an important role. First, we make a program/software and intent some mutant in this program/software, find most critical path and optimise test cases using genetic algorithm for the unit testing. Initially generated test cases are refined using genetic algorithm. We use a mutant function for measuring the adequacy of the test case set. The given mutant function is used to calculate a mutant score. We have achieved 100% path coverage and boundary coverage using mutation testing. The objective is to produce a set of good test cases for killing one or more undesired mutants and produces different mutant from original software/program. Unlike simple algorithms, genetic algorithms provide suitability for reducing the data generation at a comparable cost. An optimised test case has been generated by proposed approach for cost reduction and revealing or killing undesired test cases.

Fault-based testing is a technique where testers anticipate errors in a system under test in order to assess or generate test cases. The idea is to have enough test cases capable of detecting these anticipated errors. This paper presents a theory and technique for generating fault-based test cases for concurrent systems. The novel idea is to generate test purposes from faults that have been injected into a model of the system under test. Such test purposes form a specification of a more detailed test case that can detect the injected fault. The theory is based on the notion of refinement. The technique is automated using the TGV test case generator and an equivalence checker of the CADP tools. A case study of testing web servers demonstrates the practicability of the approach.KeywordsModel CheckerMutation TestingTesting TheoryTest PurposeLabel Transition SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In order to solve the current problems( expensive testing cost and low efficiency of test case generation) in mutation test for Simulink models,a mutation testing process and an optimized set of mutation operators were proposed for Simulink models based on program mutation according to the current mutation operators for the Simulink models. Experiments show that this set of mutation operators can effectively reduce the generation number of mutation models without prejudice to the mutation score of testing case set,thus it will effectively save the testing cost. Then a search-based test case generation method for Simulink models mutation testing was described. The test case generation problem was transformed into the objective function minimization problem,and the test cases which can kill the mutation models were ultimately obtained through the optimization of objective function by algorithm of simulated annealing. Finally,the application of a typical case for the method verified the correctness and effectiveness.

Testing Deep Learning (DL) systems is a complex task as they do not behave like traditional systems would, notably because of their stochastic nature. Nonetheless, being able to adapt existing testing techniques such as Mutation Testing (MT) to DL settings would greatly improve their potential verifiability. While some efforts have been made to extend MT to the Supervised Learning paradigm, little work has gone into extending it to Reinforcement Learning (RL) which is also an important component of the DL ecosystem but behaves very differently from SL. This paper builds on the existing approach of MT in order to propose a framework, RLMutation, for MT applied to RL. Notably, we use existing taxonomies of faults to build a set of mutation operators relevant to RL and use a simple heuristic to generate test cases for RL. This allows us to compare different mutation killing definitions based on existing approaches, as well as to analyze the behavior of the obtained mutation operators and their potential combinations called Higher Order Mutation(s) (HOM). We show that the design choice of the mutation killing definition can affect whether or not a mutation is killed as well as the generated test cases. Moreover, we found that even with a relatively small number of test cases and operators we manage to generate HOM with interesting properties which can enhance testing capability in RL systems.

State-based testing (SBT) is known as deriving test cases from state machines and examining the dynamic behaviour of the system. It helps to identify various types of state-based faults within a system under test (SUT). For SBT, test cases are generated from state chart diagrams based on various coverage criteria such as All Transition, Round Trip Path, All Transition Pair, All Transition Pair with length 2, All Transition Pair with length 3, All Transition Pair of length 4 and Full Predicate. This article discuses a number of coverage criteria at the design level to find out various types of state-based faults in SBT. First, the intermediate graph is generated from a state chart diagram using an XML parser. The graph is traversed based on the given coverage criteria to generate a sequence of test cases. Then, mutation testing and sneak-path testing are applied on the generated test cases to check the effectiveness of the generated test suite. These two are common methods for checking the effectiveness of test cases. Mutation testing helps in the number of seeded errors covered whereas sneak-path testing basically helps to examine the unspecified behavior of the system. In round trip path (RTP), it is not possible to cover all paths. All transition is not an adequate level of fault detection with more execution time compared to all transition pair (ATP) with length 4 (LN4). In the discussion, ATP with LN4 is the best among all coverage criteria. SBT can able to detect various state-based faults-incorrect transition, missing transition, missing or incorrect event, missing or incorrect action, extra missing or corrupt state, which are difficult to detect in code-based testing. Most of these state-based faults can be avoided, if the testing is conducted at the early phase of design.

SummaryThis article presents the techniques and results of a novel model‐based test case generation approach that automatically derives test cases from UML state machines. The main contribution of this article is the fully automated fault‐based test case generation technique together with two empirical case studies derived from industrial use cases. Also, an in‐depth evaluation of different fault‐based test case generation strategies on each of the case studies is given and a comparison with plain random testing is conducted. The test case generation methodology supports a wide range of UML constructs and is grounded on the formal semantics of Back's action systems and the well‐known input–output conformance relation. Mutation operators are employed on the level of the specification to insert faults and generate test cases that will reveal the faults inserted. The effectiveness of this approach is shown and it is discussed how to gain a more expressive test suite by combining cheap but undirected random test case generation with the more expensive but directed mutation‐based technique. Finally, an extensive and critical discussion of the lessons learnt is given as well as a future outlook on the general usefulness and practicability of mutation‐based test case generation. Copyright © 2014 John Wiley & Sons, Ltd.

Software testing is one activity for Software Quality Assurance (SQA). One way to test is through Black box test cases at design level. Since UML is a de-facto design language and is one of the most used design language now a day. Therefore, there is need to design black box test cases using UML behavioral diagrams. Object Constraint Language (OCL) is a formal language to write constraints on UML diagrams as it is unambiguous. Although test cases against robustness have been designed through OCL expressions but no test cases as yet have been designed against security specific authentication, access control and availability. Specification and testing of security specific authentication, access Control and availability through OCL. An experiment is performed with a hypothesis ‘if Security Specific Authentication, Access Control and Availability is specified through OCL in state diagram then Test cases can be designed’. We have specifiedsecurity in OCL then same OCL expressions are used to model security in UML state diagram. Furthermore, mutation testing is performed on OCL expressions. Then ECP is done using same extracted mutants. Security specific authentication, access control and availability is specified in OCL and then test cases are designed using a black box testing technique Equivalence Class Partitioning. Hypothesis results shows that one can design test cases through OCL. This thesis shows that Specification and Testing of Security specific authentication, access control and availability through OCL.

Testing provides a primary means for assuring software in safety-critical systems. To demonstrate, particularly to a certification authority, that sufficient testing has been performed, it is necessary to achieve the test coverage levels recommended or mandated by safety standards and industry guidelines. Mutation testing provides an alternative or complementary method of measuring test sufficiency, but has not been widely adopted in the safety-critical industry. In this study, we provide an empirical evaluation of the application of mutation testing to airborne software systems which have already satisfied the coverage requirements for certification. Specifically, we apply mutation testing to safety-critical software developed using high-integrity subsets of C and Ada, identify the most effective mutant types, and analyze the root causes of failures in test cases. Our findings show how mutation testing could be effective where traditional structural coverage analysis and manual peer review have failed. They also show that several testing issues have origins beyond the test activity, and this suggests improvements to the requirements definition and coding process. Our study also examines the relationship between program characteristics and mutation survival and considers how program size can provide a means for targeting test areas most likely to have dormant faults. Industry feedback is also provided, particularly on how mutation testing can be integrated into a typical verification life cycle of airborne software.

Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite.

Metamorphic testing is a well known approach to tackle the oracle problem in software testing. This technique requires the use of source test cases that serve as seeds for the generation of follow-up test cases. Systematic design of test cases is crucial for the test quality. Thus, source test case generation strategy can make a big impact on the fault detection effectiveness of metamorphic testing. Most of the previous studies on metamorphic testing have used either random test data or existing test cases as source test cases. There has been limited research done on systematic source test case generation for metamorphic testing. This paper provides a comprehensive evaluation on the impact of source test case generation techniques on the fault finding effectiveness of metamorphic testing. We evaluated the effectiveness of line coverage, branch coverage, weak mutation and random test generation strategies for source test case generation. The experiments are conducted with 77 methods from 4 open source code repositories. Our results show that by systematically creating source test cases, we can significantly increase the fault finding effectiveness of metamorphic testing. Further, in this paper we introduce a simple metamorphic testing tool called "METtester" that we use to conduct metamorphic testing on these methods.

This research focuses on the Kawn Subscriptions Manager app, which was developed to help Food and Beverage (F&B) businesses manage customer subscriptions. Currently, the Kawn Subscriptions Manager application does not have a standardized test suite that has been validated. Therefore, in this research, Model-Based Testing (MBT) is applied in testing the Kawn Subscriptions Manager application as a testing method to produce a specific and technical test suite. MBT is a testing method based on the behavioral model of the system being tested. Then, to measure the adequacy of test cases generated by MBT, mutation testing is used. The model is created using the GraphWalker tool and testing is done automatically using Selenium and testNG in the Eclipse IDE text editor. From the results of the study, it is known that MBT testing generates test cases from the behavior model of the function being tested. Testing with MBT shows that no failures were found in the tested functions. Then the average mutation score is 72.18% which indicates that the test cases generated by MBT are adequate or in the medium category. However, it is important to note that while MBT was successful in generating test cases that examined the application's behavior, it was found that the method was unable to detect failures caused by mutants that did not significantly affect the application's behavior. This finding suggests that while MBT can be an effective tool for testing key functions, additional methods are needed to identify failures that may result from minor or insignificant changes to the application code.

This chapter builds the foundation of software testing techniques by classifying the various testing approaches and testing coverage criteria. It gradually advances in the concepts and process of Mutation Testing and its application areas. Mutation testing has been applied at both the source code level and specification level of the software under test. Mutation testing, when applied to the source code, is named as Program Mutation. Similarly, when applied to the specifications, it is named as Specification Mutation. The relevant Mutation Testing tools available for different programming languages for both program and Specification Mutations are hereby listed. Owing to the high cost incurred in applying Mutation Testing to industrial needs, the on-going endeavors of the researchers in the area are elaborated here. Applying nature-inspired algorithms along with Mutation Testing for data generation/selection/minimization is an upcoming area of research. Search based Mutation Testing (SBMT) applies evolutionary techniques like Genetic Algorithms or other metaheuristic approaches for automating the tasks associated with mutation testing, which otherwise requires a lot of human effort, thus, making it a practical approach. This chapter concludes by giving the seminal recent advancements in the area.

A testing criterion is a predicate to be satisfied and generally addresses two important questions related to: 1) the selection of test cases capable of revealing as many faults as possible; and 2) the evaluation of a test set to consider the test ended. Studies show that fault based criteria, such as mutation testing, are very efficacious, but very expensive in terms of the number of test cases. Mutation testing uses mutation operators to generate alternatives for the program P under test. The goal is to derive test cases to producing different behaviours in P and its alternatives. This approach usually does not allow the test of interaction between faults since the alternative differs from P by a simple modification. This work explores the use of Genetic Programming (GP) to derive alternatives for testing P and describes two GP-based test procedures for selection and evaluation of test data. Experimental results show the GP approach applicability and allow comparison with mutation testing.

Mutation testing in test suite generation using separate bacterial memetic evolutionary algorithm in IoT