Automated Test Generation and Mutation Testing for Alloy

Sullivan, Allison; Wang, Kaiyuan; Zaeem, Razieh Nokhbeh; Khurshid, Sarfraz

doi:10.1109/icst.2017.31

Cited by 37 publications

(18 citation statements)

References 43 publications

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“…Mutation testing has also been applied on Alloy models. Sullivan et al [306] performed mutation testing in declarative programming paradigm (Alloy language) to support test case generation and showed that it is robust at revealing real faults.…”

Section: Model-based Testingmentioning

confidence: 99%

Mutation Testing Advances: An Analysis and Survey

Papadakis

Kintis

Zhang

et al. 2019

Advances in Computers

357

343

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Mutation testing realises the idea of using artificial defects to support testing activities. Mutation is typically used as a way to evaluate the adequacy of test suites, to guide the generation of test cases and to support experimentation.Mutation has reached a maturity phase and gradually gains popularity both in academia and in industry. 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. It also collects advices on best practices related to the use of mutation in empirical studies of software testing. Thus, giving the reader a 'mini-handbook'-style roadmap for the application of mutation testing as experimental methodology.

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Section: Model-based Testingmentioning

confidence: 99%

Mutation Testing Advances: An Analysis and Survey

Papadakis

Kintis

Zhang

et al. 2019

Advances in Computers

357

343

View full text Add to dashboard Cite

show abstract

“…MuAlloy [21,27] automatically generates mutants and filters out mutants that are semantically equivalent to the original base model. Table 1 shows the mutation operators supported in MuAlloy.…”

Section: Mualloymentioning

confidence: 99%

“…To evaluate iAlloy we conduct experiments using two sets of Alloy models that have multiple versions. One set, termed mutant version set, uses simulated evolving Alloy models where different versions are created using the MuAlloy [21,27] tool for generating mutants with small syntactic modifications of the given base Alloy models. This set includes 24 base Alloy models and 5 mutant versions for each base model.…”

Section: Introductionmentioning

confidence: 99%

“…In this section, we first introduce Alloy [5] based on an example which we use through the paper. Then, we describe MuAlloy [21,27] -a mutation testing framework for Alloy, which we apply to create different versions of an Alloy model to simulate model evolutions. Finally, we briefly describe regression test selection (RTS) for imperative code.…”

Section: Introductionmentioning

confidence: 99%

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Incremental Analysis of Evolving Alloy Models

Wang

Gligoric

et al. 2019

Tools and Algorithms for the Construction and Analysis of Systems

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Alloy is a well-known tool-set for building and analyzing software designs and models. Alloy's key strengths are its intuitive notation based on relational logic, and its powerful analysis engine backed by propositional satisfiability (SAT) solvers to help users find subtle design flaws. However, scaling the analysis to the designs of real-world systems remains an important technical challenge. This paper introduces a new approach, iAlloy, for more efficient analysis of Alloy models. Our key insight is that users often make small and frequent changes and repeatedly run the analyzer when developing Alloy models, and the development cost can be reduced with the incremental analysis over these changes. iAlloy is based on two techniques-a static technique based on a lightweight impact analysis and a dynamic technique based on solution re-use-which in many cases helps avoid potential costly SAT solving. Experimental results show that iAlloy significantly outperforms Alloy analyzer in the analysis of evolving Alloy models with more than 50% reduction in SAT solver calls on average, and up to 7x speedup.

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“…Mutation testing mainly used to mutate source code or programming language feature [9], but mutation testing is also capable in testing software design [10], to mutate software model [11], [12], or even used as a tool for teaching software testing [13]. The research about mutation testing has been developed to expand its practice to a more complex application [14] [15] or specific platform (i.e Android [16], [17] and web application [18]).…”

Section: Mutation Testing (Problem)mentioning

confidence: 99%

Coevolution of Second-order-mutant

Tuloli

Sitohang

Hendradjaya

2018

IJECE

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<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>

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Automated Test Generation and Mutation Testing for Alloy

Cited by 37 publications

References 43 publications

Mutation Testing Advances: An Analysis and Survey

Mutation Testing Advances: An Analysis and Survey

Incremental Analysis of Evolving Alloy Models

Coevolution of Second-order-mutant

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