Searching and evolving test cases using moth flame optimisation for mutation testing

2021 ◽  
Vol 9 (3) ◽  
pp. 276
Author(s):  
Shweta Rani ◽  
Bharti Suri
Keyword(s):  
Mutation Testing ◽  
Test Cases

scholarly journals Coevolution of Second-order-mutant

2018 ◽  
Vol 8 (5) ◽  
pp. 3238
Author(s):  
Mohamad Syafri Tuloli ◽  
Benhard Sitohang ◽  
Bayu Hendradjaya
Keyword(s):  
Genetic Algorithm ◽  
Optimization Method ◽  
Mutation Testing ◽  
Second Order ◽  
Real Case ◽  
Test Case ◽  
Test Cases ◽  
Single Population ◽  
Testing Problem

<span>One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneously using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multi-problem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.</span>

scholarly journals Speeding-up mutation testing via data compression and state infection

2016 ◽  
Author(s):  
Qianqian Zhu ◽  
Annibale Panichella ◽  
Andy Zaidman
Keyword(s):  
Data Compression ◽  
Formal Concept Analysis ◽  
Computational Cost ◽  
Mutation Testing ◽  
Formal Concept ◽  
Test Cases ◽  
Vast Number ◽  
Speed Up ◽  
Novel Method ◽  
Initial Results

Mutation testing is widely considered as a high-end test criterion due to the vast number of mutants it generates. Although many efforts have been made to reduce the computational cost of mutation testing, its scalability issue remains in practice. In this paper, we introduce a novel method to speed up mutation testing based on state infection information. In addition to filtering out uninfected test executions, we further select a subset of mutants and a subset of test cases to run leveraging data-compression techniques. In particular, we adopt Formal Concept Analysis (FCA) to group similar mutants together and then select test cases to cover these mutants. To evaluate our method, we conducted an experimental study on six open source Java projects. We used EvoSuite to automatically generate test cases and to collect mutation data. The initial results show that our method can reduce the execution time by 83.93% with only 0.257% loss in precision.

Coverage Criteria for State-Based Testing

2019 ◽  
Vol 10 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Sonali Pradhan ◽  
Mitrabinda Ray ◽  
Srikanta Patnaik
Keyword(s):  
Dynamic Behaviour ◽  
Mutation Testing ◽  
Test Cases ◽  
State Machines ◽  
Round Trip ◽  
Coverage Criteria ◽  
State Chart ◽  
Path Testing ◽  
The Given ◽  
Intermediate Graph

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.

scholarly journals Mutation Testing Approach to Negative Testing

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Joanna Strug
Keyword(s):  
Mutation Testing ◽  
Test Cases ◽  
Generation Process ◽  
System Failures ◽  
Mutation Operators ◽  
Generic Framework ◽  
Wide Range ◽  
Testing Approach

Negative testing deals with an important problem of assessing a system ability to handle unexpected situations. Such situations, if unhandled, may lead to system failures that in some cases can have catastrophic consequences. This paper presents a mutation testing-based approach for generation of test cases supporting negative testing. Application of this approach can provide, in a systematic and human-unbiased way, test cases effectively testing wide range of unexpected situations. Thus, it can contribute to improvement of a tested system. The paper formally defines mutation operators used to control the generation process, describes a generic framework for the generation and execution of the test cases, and explains how to interpret results.

scholarly journals Mutation testing with hyperproperties

2021 ◽  
Author(s):  
Andreas Fellner ◽  
Mitra Tabaei Befrouei ◽  
Georg Weissenbacher
Keyword(s):  
Model Checking ◽  
Model Transformation ◽  
Current Model ◽  
Original System ◽  
Mutation Testing ◽  
Test Case ◽  
Test Cases ◽  
Modeling Languages ◽  
Deterministic Models ◽  
Testing Tool

AbstractWe present a new method for model-based mutation-driven test case generation. Mutants are generated by making small syntactical modifications to the model or source code of the system under test. A test case kills a mutant if the behavior of the mutant deviates from the original system when running the test. In this work, we use hyperproperties—which allow to express relations between multiple executions—to formalize different notions of killing for both deterministic as well as non-deterministic models. The resulting hyperproperties are universal in the sense that they apply to arbitrary reactive models and mutants. Moreover, an off-the-shelf model checking tool for hyperproperties can be used to generate test cases. Furthermore, we propose solutions to overcome the limitations of current model checking tools via a model transformation and a bounded SMT encoding. We evaluate our approach on a number of models expressed in two different modeling languages by generating tests using a state-of-the-art mutation testing tool.

Automatic Generation of Test Cases from Formal Specifications using Mutation Testing

2021 ◽  
Author(s):  
Roman Jaramillo Cajica ◽  
Raul Ernesto Gonzalez Torres ◽  
Pedro Mejia Alvarez
Keyword(s):  
Automatic Generation ◽  
Mutation Testing ◽  
Test Cases ◽  
Formal Specifications

Coverage Criteria for State-Based Testing

2022 ◽  
pp. 1222-1244
Author(s):  
Sonali Pradhan ◽  
Mitrabinda Ray ◽  
Srikanta Patnaik
Keyword(s):  
Early Phase ◽  
Mutation Testing ◽  
Test Cases ◽  
State Machines ◽  
Round Trip ◽  
Coverage Criteria ◽  
State Chart ◽  
Path Testing ◽  
The Given ◽  
Intermediate Graph

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.

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