A mapping study on mutation testing for mobile applications

Hearing loss is one of the most common health problems today, it can appear at any age and the causes are varied, in order to prevent it or adapt to the changes brought about by the hearing impairment, it is necessary to diagnose it in time. The technology in terms of applications for health care smartphones has constantly evolved, so that today play an important role and are among the most downloaded from application stores, several of these applications are the diagnosis of hearing loss and use the method of pure tones. In this study a Systematic Mapping of Literature SMS (Systematic Mapping Study) is made to look for mobile applications that use other diagnostic methods that offer similar or better results, of the 13 applications found, 11 used the method of pure tones and in only 2 of them was implemented the speech audiometry (word recognition), concludes that diagnostic hearing loss tests based on mobile applications are reliable alternatives to conventional audiometric systems, and that pure tone thresholds alone are an incomplete assessment of hearing, and there is a need to develop new hearing measurement methods and combine them with other methods to complement the diagnosis. Resumen: La pérdida de la audición es uno de los problemas de salud más comunes en la actualidad, puede aparecer a cualquier edad y las causas son variadas, para poder prevenirla o adaptarse a los cambios que conlleva la deficiencia auditiva, es necesario diagnosticarla a tiempo. La tecnología en cuanto a aplicaciones para smartphones de asistencia de salud ha evolucionado constantemente, tal es así que hoy en día juegan un papel importante y son de las más descargadas de las tiendas de aplicaciones, varias de esas aplicaciones son las de diagnóstico de pérdida auditiva y utilizan el método de los tonos puros. En este estudio se hace un Mapeo Sistemático de Literatura SMS (Systematic Mapping Study) para buscar aplicaciones móviles que utilicen otros métodos de diagnóstico que ofrezcan similares o mejores resultados, de las 13 aplicaciones encontradas, 11 utilizaron el método de los tonos puros y en solo 2 de ellas se implementó la logoaudiometria (reconocimiento de palabras), por lo que se concluye que las pruebas de diagnóstico de pérdida auditiva basadas en aplicaciones móviles, son alternativas confiables a los sistemas de audiometría convencionales, y que los umbrales de tonos puros por sí solos son una evaluación incompleta de la audición, y existe la necesidad de desarrollar nuevos métodos de medición de audición y combinarlos con otros métodos para complementar el diagnóstico.

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An Empirical Analysis of Mobile Apps’ Popularity Metrics in Mobile Software Ecosystems

10.5753/sbqs.2016.15123 ◽

2016 ◽

Author(s):

Ludymila L. A. Gomes ◽

Awdren L. Fontão ◽

Allan J. S. Bezerra ◽

Arilo C. Dias-Neto

Keyword(s):

Mobile Applications ◽

User Satisfaction ◽

Mobile Apps ◽

Mapping Study ◽

Mobile Platforms ◽

Technical Literature ◽

Software Ecosystem ◽

Software Ecosystems ◽

Mobile Software

The growing of mobile platforms in the last years has changed the software development scenario and challenged developers around the world in building successful mobile applications (apps). Users are the core of a mobile software ecosystem (MSECO). Thus, the quality of an app would be related to the user satisfaction, which could be measured by its popularity in App Store. In this paper, we describe the results of a mapping study that identified and analyzed how metrics on apps’ popularity have been addressed in the technical literature. 18 metrics were identified as related to apps’ popularity (users rating and downloads the most cited). After that, we conducted a survey with 47 developers acting within the main MSECOs (Android, iOS and Windows) in order to evaluate these 18 metrics regarding their usefulness to characterize app's popularity. As results, we observed developers understand the importance of metrics to indicate popularity of apps in a different way when compared to the current research.

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Security Aspects for Mutation Testing in Mobile Applications

10.1007/978-981-16-4284-5_2 ◽

2021 ◽

pp. 17-27

Author(s):

Naived George Eapen ◽

A. Raghavendra Rao ◽

Debabrata Samanta ◽

Nismon Rio Robert ◽

Ramkumar Krishnamoorthy ◽

...

Keyword(s):

Mobile Applications ◽

Mutation Testing

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Automated functional testing of mobile applications: a systematic mapping study

Software Quality Journal ◽

10.1007/s11219-018-9418-6 ◽

2018 ◽

Vol 27 (1) ◽

pp. 149-201 ◽

Cited By ~ 6

Author(s):

Porfirio Tramontana ◽

Domenico Amalfitano ◽

Nicola Amatucci ◽

Anna Rita Fasolino

Keyword(s):

Mobile Applications ◽

Systematic Mapping Study ◽

Functional Testing ◽

Mapping Study ◽

Systematic Mapping

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Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

Mobile Information Systems ◽

10.1155/2021/6677075 ◽

2021 ◽

Vol 2021 ◽

pp. 1-24

Author(s):

Abderrahim El hafidy ◽

Taoufik Rachad ◽

Ali Idri ◽

Ahmed Zellou

Keyword(s):

Machine Learning ◽

Mobile Applications ◽

Mobile Apps ◽

Driving Behavior ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Systematic Mapping Study ◽

Mapping Study ◽

Systematic Mapping ◽

Learning Techniques

Many research works and official reports approve that irresponsible driving behavior on the road is the main cause of accidents. Consequently, responsible driving behavior can significantly reduce accidents’ number and severity. Therefore, in the research area as well as in the industrial area, mobile technologies are widely exploited in assisting drivers in reducing accident rates and preventing accidents. For instance, several mobile apps are provided to assist drivers in improving their driving behavior. Recently and thanks to mobile cloud computing, smartphones can benefit from the computing power of servers in the cloud for executing machine learning algorithms. Therefore, many mobile applications of driving assistance and control are based on machine learning techniques to adjust their functioning automatically to driver history, context, and profile. Additionally, gamification is a key element in the design of these mobile applications that allow drivers to develop their engagement and motivation to improve their driving behavior. To have an overview concerning existing mobile apps that improve driving behavior, we have chosen to conduct a systematic mapping study about driving behavior mobile apps that exist in the most common mobile apps repositories or that were published as research works in digital libraries. In particular, we should explore their functionalities, the kinds of collected data, the used gamification elements, and the used machine learning techniques and algorithms. We have successfully identified 220 mobile apps that help to improve driving behavior. In this work, we will extract all the data that seem to be useful for the classification and analysis of the functionalities offered by these applications.

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A systematic mapping study on higher order mutation testing

Journal of Systems and Software ◽

10.1016/j.jss.2019.04.031 ◽

2019 ◽

Vol 154 ◽

pp. 92-109 ◽

Cited By ~ 1

Author(s):

Jackson Antonio do Prado Lima ◽

Silvia Regina Vergilio

Keyword(s):

Mutation Testing ◽

Higher Order ◽

Systematic Mapping Study ◽

Mapping Study ◽

Systematic Mapping

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Intelligent Mobile Applications: A Systematic Mapping Study

Mobile Information Systems ◽

10.1155/2020/6715363 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Taoufik Rachad ◽

Ali Idri

Keyword(s):

Mobile Technology ◽

Mobile Applications ◽

Daily Life ◽

Systematic Mapping Study ◽

Mapping Study ◽

Systematic Mapping ◽

E Learning ◽

Application Fields ◽

Research Type

Smart mobiles as the most affordable and practical ubiquitous devices participate heavily in the enhancement of our daily life by the use of many convenient applications. However, the significant number of mobile users in addition to their heterogeneity (different profiles and contexts) obligates developers to enhance the quality of their apps by making them more intelligent and more flexible. This is realized mainly by analyzing mobile user’s data. Machine learning (ML) technology provides the methodology and techniques needed to extract knowledge from data to facilitate decision-making. Therefore, both developers and researchers affirm the benefits of combining ML techniques and mobile technology in several application fields as e-health, e-learning, e-commerce, and e-coaching. Thus, the purpose of this paper is to have an overview of the use of ML techniques in the design and development of mobile applications. Therefore, we performed a systematic mapping study of papers published on this subject in the period between 1 January 2007 and 31 December 2019. A total number of 71 papers were selected, studied, and analyzed according to the following criteria, year, sources and channel of publication, research type, and methods, kind of collected data, and finally adopted ML models, tasks, and techniques.

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Quality of Experience in Mobile Applications: A Systematic Mapping of Metrics and Tools

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v14i08.12819 ◽

2020 ◽

Vol 14 (08) ◽

pp. 126

Author(s):

Antonio Collazo Garcia ◽

Sandra Casas

Keyword(s):

Mobile Applications ◽

Quality Of Experience ◽

Service Providers ◽

Mobile Technologies ◽

Systematic Mapping Study ◽

Mapping Study ◽

Systematic Mapping ◽

User Perceptions ◽

Analysis Tools

Context: Quality of Experience (QoE) enables the description of user perceptions about the performance of a particular application or service. In the mobile computing context, it is an important measure for service providers and users, since QoE makes it possible to improve it and make it more competitive to achieve user fidelity. In turn, the importance of QoE in mobile technologies increases due to the various factors that affect the applications that run on mobile devices. Objective: The purpose of this study is to identify the metrics and tools relevant to the scientific community for the QoE analysis of mobile applications. Method: A systematic mapping study was conducted. Results: From a total of 751 studies, 33 were selected, and 13 metrics and 15 mobile QoE analysis tools were identified. Conclusions: The existing mobile QoE analysis tools collect and calculate metrics automatically, combining objective and subjective metrics. However, they present limited approaches, making it difficult to carry out an integral analysis of the applications.

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A systematic mapping study addressing the reliability of mobile applications: The need to move beyond testing reliability

Journal of Systems and Software ◽

10.1016/j.jss.2021.111166 ◽

2021 ◽

pp. 111166

Author(s):

Chathrie Wimalasooriya ◽

Sherlock A. Licorish ◽

Daniel Alencar da Costa ◽

Stephen G. MacDonell

Keyword(s):

Mobile Applications ◽

Systematic Mapping Study ◽

Mapping Study ◽

Systematic Mapping

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Development of Mobile Applications with Functions in the Cloud through the Model Driven Approach: A Systematic Mapping Study

CLEI electronic journal ◽

10.19153/cleiej.20.3.6 ◽

2017 ◽

Vol 20 (3) ◽

Cited By ~ 1

Author(s):

Emanuel Sanchiz ◽

Magalí González ◽

Nathalie Aquino ◽

Luca Cernuzzi

Keyword(s):

Mobile Applications ◽

Service Providers ◽

Systematic Mapping Study ◽

Mapping Study ◽

Systematic Mapping ◽

Model Driven Development ◽

Model Driven ◽

Industrial Environments ◽

Model Driven Approach ◽

Complex Cases

Currently, a growing interest is being caused by mobile applications which have functions implemented in the cloud (MobileApps-FC). Improvements related to the portability of these applications among different platforms and different service providers are a critical need. Model Driven Development (MDD) constitutes one of the alternatives to address portability issues. This work presents a systematic mapping study that analyzes different proposals that apply MDD to the development of MobileApps-FC and that, at the same time, consider the improvement of the portability of such applications. Even though we have identified just a few studies related to our subject of interest, the validation experiences that are presented in them, encourage the adoption of MDD to address portability issues. However, further validation experiences that consider more complex cases in industrial environments will be required to justify the benefits of MDD in a substantial manner.

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