Automated Testing of Timeliness : A Case Study

Nilsson, Robert; Offutt, Jeff

doi:10.1109/ast.2007.5

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…Speaking about real time, mutation testing has been applied to many traditional programming languages that have been growing and now they can be applied to real‐time systems, ie, Java, C, and Ada . There are also mutation testing studies, which are focused on real‐time systems, ie, Nilsson et al have published works where they proposed a model‐based mutation testing, which analyses models of real‐time systems to generate test cases for the automated testing of timeliness; in another work of the aforementioned authors, they show a method using heuristic‐driven simulation to generate test cases (based on the previous model); and in another work of the aforementioned authors, they evaluated a mutation‐based framework for the automated testing of timeliness by applying it on a small control system.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary mutation testing for IoT with recorded and generated events

2018

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Mutation testing is a testing technique that has been applied successfully to several programming languages. Despite its benefits for software testing, the high computational cost of mutation testing has kept it from being widely used. Several refinements have been proposed to reduce its cost by reducing the number of generated mutants; one of those is evolutionary mutation testing (EMT). Evolutionary mutation testing aims at generating a reduced set of mutants with an evolutionary algorithm, which searches for potentially equivalent and difficult to kill mutants that help improve the test suite. Evolutionary mutation testing has been evaluated in two contexts so far, ie, web service compositions and object-oriented C++ programmes. This study explores its performance when applied to event processing language queries of various domains. This study also considers the impact of the test data, since a lack of events or the need to have specific values in them can hinder testing. The effectiveness of evolutionary mutation testing with the original test data generators and the new internet of things test event generator tool is compared in multiple case studies. KEYWORDSCEP, event processing language, evolutionary mutation testing, genetic algorithm, guided evolutionary mutation testing, internet of things, IoT-TEG 640

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Section: Related Workmentioning

confidence: 99%

Evolutionary mutation testing for IoT with recorded and generated events

2018

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“…The work by Nilsson and Offut [19] uses patterns of potential faults to detect missed scheduling of sporadic and periodic time-critical tasks. Our approach employs a similar idea, in which the different refresh rates of asynchronously updated context variables may trigger incorrect decisions in an application's adaptation behavior.…”

Section: Related Workmentioning

confidence: 99%

Model-based fault detection in context-aware adaptive applications

Sama

Rosenblum

Wang

et al. 2008

Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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Applications running on mobile devices are heavily contextaware and adaptive, leading to new analysis and testing challenges as streams of context values drive these applications to undesired configurations that are not easily exposed by existing validation techniques. We address this challenge by employing a finite-state model of adaptive behavior to enable the detection of faults caused by (1) erroneous adaptation logic, and (2) asynchronous updating of context information, which leads to inconsistencies between the external physical context and its internal representation within an application. We identify a number of adaptation fault patterns, each describing a class of faulty behaviors that we detect automatically by analyzing the system's adaptation model. We illustrate our approach on a simple but realistic application in which a cellphone's configuration profile is changed automatically based on the user's location, speed and surrounding environment.

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“…The study involves the generation of mutants for the 15 selected applications in study 1 to test them and measure the mutation score (MS). Several mutants generation methods/operators were defined and developed for mutation testing such as muJava [56], the technique by Nilsson and Offutt [57] that ware designed for desktop applications. However, these techniques are not suitable for mobile apps.…”

Section: B Study 2: Mutation Testing (Rq2)mentioning

confidence: 99%

AMOGA: A Static-Dynamic Model Generation Strategy for Mobile Apps Testing

et al. 2019

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In the past few years, mobile devices have been increasingly replacing traditional computers as their capabilities such as CPU computation, memory, RAM size, and many more, are being enhanced almost to the level of conventional computers. These capabilities are being exploited by mobile apps developers to produce apps that offer more functionalities and optimized performance. To ensure acceptable quality and to meet their specifications (e.g., design), mobile apps need to be tested thoroughly. As the testing process is often tedious, test automation can be the key to alleviating such laborious activities. In the context of the Android-based mobile apps, researchers and practitioners have proposed many approaches to automate the testing process mainly on the creation of the test suite. Although useful, most existing approaches rely on reverse engineering a model of the application under test for test case creation. Often, such approaches exhibit a lack of comprehensiveness as the application model does not capture the dynamic behavior of the applications extensively due to the incompleteness of reverse engineering approaches. To address this issue, this paper proposes AMOGA, a strategy that uses a hybrid, static-dynamic approach for generating user interface model from mobile apps for modelbased testing. AMOGA implements a novel crawling technique that uses the event list of UI element associated with each event to dynamically exercise the events ordering at the run-time to explore the applications' behavior. An experimental evaluation was performed to assess the effectiveness of our strategy by measuring the code coverage and the fault detection capability through the use of mutation testing concept. Results of the experimental assessment showed that AMOGA represents an alternative approach for model-based testing of mobile apps by generating comprehensive models to improve the coverage of the applications. The strategy proved its effectiveness by achieving high code coverage and mutation score for different applications.

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Automated Testing of Timeliness : A Case Study

Cited by 12 publications

References 13 publications

Evolutionary mutation testing for IoT with recorded and generated events

Evolutionary mutation testing for IoT with recorded and generated events

Model-based fault detection in context-aware adaptive applications

AMOGA: A Static-Dynamic Model Generation Strategy for Mobile Apps Testing

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