Software analytics tools: an intentional view

Software analytic tools consume big amounts of data coming from either (or both) the software development process or the system usage and aggregate them into indicators which are rendered to different types of stakeholders, also offering them a portfolio of techniques and capabilities such as what-if analysis, prediction and alerts. Precisely, the variety of stakeholders and the different goals they pursue justifies the convenience of performing an intentional analysis of the use of software analytics tools. With this aim, we first enumerate the different stakeholders and identify their intentional relationships with software analytics tools in the form of dependencies. Then, we focus on one particular stakeholder, namely the requirements engineer, and identify further intentional elements represented in a strategic rationale model. The resulting model provides an abstract view of the domain which may help stakeholders when deciding on the adoption of software analytic tools in their particular context.This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Reverse Engineering Mobile Apps for Model Generation Using a Hybrid Approach

The popularity of mobile devices is ever increasing which led to rapid increase in the development of mobile applications. Model-Based testing can improve the quality of mobile application but the models are not always available or are of inadequate quality. Reverse engineering approaches are used to automatically generate model from the GUI of mobile applications for model-based testing. This paper proposes a hybrid approach for reverse engineering mobile applications which exploit the capabilities of both static and dynamic approaches while trying to maximize the quality of the generated models. The insight of this approach is to use static analysis on app’s source to identify supported events. The generated events can be used to dynamically explore an app at run-time to generate a state model of the app’s GUI. The preliminary results from our approach indicated that the technique can generate high quality models from android apps

A Hybrid Approach for Reverse Engineering GUI Model from Android Apps for Automated Testing

Nowadays, smartphone users are increasingly relying on mobile applications to complete most of their daily tasks. As such, mobile applications are becoming more and more complex. Therefore, software testers can no longer rely on manual testing methods to test mobile applications. Automated model-based testing techniques are recently used to test mobile applications. However, the models generated by existing techniques are of insufficient quality. This paper proposed a hybrid technique for reverse engineering graphical user interface (GUI) model from mobile applications. It performs static analysis of application’s bytecode to extract GUI information followed by a dynamic crawling to systematically explore and reverse engineer a model of the application under test. A case study was performed on real-world mobile apps to evaluate the effectiveness of the technique. The results showed that the proposed technique can generate a model with high coverage of mobile apps behaviour

Identifying User Suggestions from Mobile App Reviews

The last few years have seen enormous growth in the use of mobile devices. This growth has fueled the development of software applications, often called apps. Mobile app developers constantly collect and analyze feedback in user reviews with the goal of improving their apps and better meeting user expectations. Due to high volume of data, manually reading user comments requires a labor-intensive effort. In this paper, we propose a framework for automatically identifying user suggestions from reviews, the information of which can be useful for next app release. Our approach uses a deep learning model with attention mechanism. Experimental results demonstrate that the proposed architecture outperforms the baseline methods.Master of Science in Information Scienc

AR-Miner: Mining informative reviews for developers from mobile app marketplace

Ministry of Education, Singapore under its Academic Research Funding Tier

Aplicación móvil para la gestión de proyectos en la constructora Inversiones SEWIMUR S.A.C.

El problema de investigación fue que la empresa Inversiones Sewimur S.A.C no dispone de un sistema en donde le permita realizar un control de los procesos de sus proyectos, es decir, no cuenta con una gestión de proyectos y la vez no tienen un área definida de sistemas en donde se pueda automatizar estos procesos ya que es una empresa en crecimiento. El objetivo de la investigación fue determinar cómo influye una Aplicación móvil en la gestión de proyectos en la constructora Inversiones Sewimur S.A.C. Al desarrollar una Aplicación móvil pueden existir distintos y nuevos métodos de desarrollo, en este caso una aplicación móvil híbrida viene siendo lo más ideal para esta empresa, por lo que normalmente los proyectos se ven mucho más en campo y se necesita llevar el control de estos en tiempo real y muy aparte otro gran beneficio es que esta es una App multiplataforma, de mucha utilidad para los distintos usuarios finales y la tecnología que ellos puedan utilizar, ya sea en celulares Android, IOS y los ordenadores que pueda tener la empresa. Para estos tipos de desarrollo de proyectos se pueden tener distintas metodologías de software que normalmente se utilizan para poder dar una garantía con respecto a la calidad del software en un tiempo determinado, por eso en este caso la metodología aplicada en este proyecto es SCRUM, la cual es una metodología ágil, exclusivo para desarrollar aplicaciones móviles en un corto plazo ya que permite la fácil comunicación entre el cliente y el equipo de trabajo. Como resultados se tuvo un aplicativo móvil híbrido, el cual permite la gestión de proyectos que determina la eficacia al poder controlar las actividades de cada proyecto en la empresa y la eficiencia en donde el aplicativo móvil permitió tener el control de los recursos que se utilizan para cada actividad y así poder ser eficaz, utilizando un mínimo de los recursos presupuestados. Como conclusión se tiene que un aplicativo móvil es de mucha utilidad para este tipo de empresas en crecimiento, los cuales buscan tener mapeado todas las actividades y procesos de los proyectos mediante un dispositivo móvil donde pueda tener el control de su presupuesto y poder tener a su alcance en el momento que lo requiera y poder registrar todas sus actividades en tiempo real

Análisis bibliométrico de los artículos científicos sobre el uso de aplicativos móviles en Obstetricia

Analiza las características bibliométricas de los artículos científicos sobre el uso de aplicativos móviles en Obstetricia. Es un estudio de tipo descriptivo, observacional, corte transversal, retrospectivo y de tipo bibliométrico. Basado en los artículos científicos de la base de datos Scopus referentes a los aplicativos móviles en obstetricia difundidos entre 2012 y 2021. Los datos se obtuvieron mediante una estrategia de búsqueda con múltiples términos MeSH y los operadores booleanos "OR" y "AND" mientras para el análisis se empleó el programa SciVal. En los resultados, se evidenció una tendencia favorable en la publicación de artículos científicos desde el año 2012 al 2021, además, la mayor proporción se encuentran en revistas del primer cuartil Q1 (50,24%). Los autores que más aportaron al crecimiento de la producción científica fueron Beth A. Payne (13), Peter von Dadelszen (13) y Laura A. Magee (13); sin embargo, el autor de mayor impacto fue Amnesty E. LeFevre con 19,1 citas por publicación y 1,56 citas ponderadas por campo. Se concluye que la mayoría de las publicaciones científica sobre aplicativos móviles en obstetricia fueron en revistas de alta calidad. Siendo, Estados Unidos y Canadá uno de los países con mayor contribución a la comunidad científica

Um estudo empírico sobre métricas de código fonte do Android API Framework

FERNANDES, Victor Henrique Magalhães. Um estudo empírico sobre métricas de código fonte do Android API Framework. 2018. 66 f., il. Trabalho de Conclusão de Curso (Bacharelado em Engenharia de Software)—Universidade de Brasília, Brasília, 2018.Métricas de código-fonte são comumente usadas para avaliar a qualidade interna de aplicativos de software. Para interpretar valores métricos, a literatura sugere valores limites, por exemplo, uma classe cujo valor métrico excede um dado limite é considerado como tendo problemas de manutenção. No entanto, não existe uma regra para identificar um limite que seja útil, pertinente e fácil de explicar. Neste trabalho, propomos medir a qualidade interna de um sistema quando ele faz parte de um ecossistema maior. Nós nos concentramos no ecossistema do Android. Nós computamos métricas conhecidas de código-fonte, como AMLOC e ACCM. Abordamos quatro aspectos: (i) analisamos a distribuição de valores métricos em várias versões do Android API Framework; (ii) extraímos limites de métricas com base nessas distribuições; (iii) utilizamos uma abordagem para extrair pontuações de qualidade para sistemas Android, comparando as distribuições métricas com as computadas no framework subjacente; e (iv) validamos essa abordagem para verificar se o índice de qualidade é realmente capaz de inferir problemas de manutenibilidade e design. Com isso, foi possível definir intervalos de referência com base na API do sistema Android, que podem auxiliar novos desenvolvedores de aplicativos, a encontrar possíveis problemas de manutenção.Source code metrics are commonly used to evaluate internal quality of software applications. To interpret metric values, the literature suggests thresholds, e.g., a class whose metric value exceeds a given threshold is considered to have maintenance problems. However, there is no rule of thumb to identify a threshold that is useful, pertinent, and easy to explain. In this paper, we propose to measure the internal quality of a system when it is part of a larger ecosystem. We focus on the Android ecosystem. We compute well-known source code metrics, such as AMLOC and ACCM. We cover four aspects: (i) we analyze the distribution of metric values in several versions of Android API Framework; (ii) we extract metric thresholds based on these distributions; (iii) we propose an approach to extract quality scores for Android systems, by comparing the metric distributions with the ones computed in the underlying framework; and (iv) we validate this approach to check whether the quality score is indeed able to infer maintainability and design problems. This enabled we to set reference ranges based on the Android system API, which can help new Application developers find possible maintenance issues

Investigating the relationship between price, rating, and popularity in the Blackberry World App Store

Context: App stores provide a software development space and a market place that are both different from those to which we have become accustomed for traditional software development: The granularity is finer and there is a far greater source of information available for research and analysis. Information is available on price, customer rating and, through the data mining approach presented in this paper, the features claimed by app developers. These attributes make app stores ideal for empirical software engineering analysis. Objective: This paper1 exploits App Store Analysis to understand the rich interplay between app customers and their developers. Method: We use data mining to extract app descriptions, price, rating, and popularity information from the Blackberry World App Store, and natural language processing to elicit each apps’ claimed features from its description. Results: The findings reveal that there are strong correlations between customer rating and popularity (rank of app downloads). We found evidence for a mild correlation between app price and the number of features claimed for the app and also found that higher priced features tended to be lower rated by their users. We also found that free apps have significantly (p-value < 0.001) higher ratings than non-free apps, with a moderately high effect size (Â12=0.68). All data from our experiments and analysis are made available on-line to support further investigations