.png){kind=icon}
.png){kind=icon}
Return
Using mutation to design tests for aspect-oriented models
.png){kind=icon}
OA
AI
DOI：10.1016/j.infsof.2016.04.007.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Context: Testing for properties such as robustness or security is complicated because their concerns are often repeated in many locations and muddled with the normal code. Such cross-cutting concerns include things like interrupt events, exception handling, and security protocols. Aspect-oriented (AO) modeling allows, developers to model the cross-cutting behavior independently of the normal behavior, thus supporting model-based testing of cross-cutting concerns. However, mutation operators defined for AO programs (source code) are usually not applicable to AO models (AGMs) and operators defined for models do not target the AO features. Objective: We present a method to design abstract tests at the aspect-oriented model level. We define mutation operators for aspect-oriented models and evaluate the generated mutants for an example system. Method: AOMs are mutated with novel operators that specifically target the AO modeling features. Test traces killing these mutant models are then generated. The generated and selected traces are abstract tests that can be transformed to concrete black-box tests and run on the implementation level, to evaluate the behavior of the woven cross-cutting concerns (combined aspect and base models). Results: This paper is a significant extension of our paper at Mutation 2015. We present a complete fault model, additional mutation operators, and a thorough analysis of the mutants generated for an example system. Conclusions: The analysis shows that some mutants are stillborn (syntactically illegal) but none is equivalent (exhibiting the same behavior as the original model). Additionally, our AOM-specific mutation operators can be combined with pre-existing operators to mutate code or models without any overlap. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Model-based testing
Aspect-oriented model
Mutation testing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.3
Papers:
3.7K
Citations:
7.7K
View more organizations