Translating Video Recordings of Mobile App Usages into Replayable Scenarios

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benefit to mobile developers. Unfortunately, automatically analyzing screen recordings presents serious challenges, due to their graphical nature, compared to other types of (textual) artifacts. To address these challenges, this paper introduces V2S, a lightweight, automated approach for translating video recordings of Android app usages into replayable scenarios. V2S is based primarily on computer vision techniques and adapts recent solutions for object detection and image classification to detect and classify user actions captured in a video, and convert these into a replayable test scenario. We performed an extensive evaluation of V2S involving 175 videos depicting 3,534 GUI-based actions collected from users exercising features and reproducing bugs from over 80 popular Android apps. Our results illustrate that V2S can accurately replay scenarios from screen recordings, and is capable of reproducing $\approx$ 89% of our collected videos with minimal overhead. A case study with three industrial partners illustrates the potential usefulness of V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software Engineering (ICSE'20), 13 page

A Survey of Performance Optimization for Mobile Applications

Nowadays there is a mobile application for almost everything a user may think of, ranging from paying bills and gathering information to playing games and watching movies. In order to ensure user satisfaction and success of applications, it is important to provide high performant applications. This is particularly important for resource constraint systems such as mobile devices. Thereby, non-functional performance characteristics, such as energy and memory consumption, play an important role for user satisfaction. This paper provides a comprehensive survey of non-functional performance optimization for Android applications. We collected 155 unique publications, published between 2008 and 2020, that focus on the optimization of non-functional performance of mobile applications. We target our search at four performance characteristics, in particular: responsiveness, launch time, memory and energy consumption. For each performance characteristic, we categorize optimization approaches based on the method used in the corresponding publications. Furthermore, we identify research gaps in the literature for future work

An Exploratory Study of Field Failures

Field failures, that is, failures caused by faults that escape the testing phase leading to failures in the field, are unavoidable. Improving verification and validation activities before deployment can identify and timely remove many but not all faults, and users may still experience a number of annoying problems while using their software systems. This paper investigates the nature of field failures, to understand to what extent further improving in-house verification and validation activities can reduce the number of failures in the field, and frames the need of new approaches that operate in the field. We report the results of the analysis of the bug reports of five applications belonging to three different ecosystems, propose a taxonomy of field failures, and discuss the reasons why failures belonging to the identified classes cannot be detected at design time but shall be addressed at runtime. We observe that many faults (70%) are intrinsically hard to detect at design-time

An Exploratory Study of Field Failures

Field failures, that is, failures caused by faults that escape the testing phase leading to failures in the field, are unavoidable. Improving verification and validation activities before deployment can identify and timely remove many but not all faults, and users may still experience a number of annoying problems while using their software systems. This paper investigates the nature of field failures, to understand to what extent further improving in-house verification and validation activities can reduce the number of failures in the field, and frames the need of new approaches that operate in the field. We report the results of the analysis of the bug reports of five applications belonging to three different ecosystems, propose a taxonomy of field failures, and discuss the reasons why failures belonging to the identified classes cannot be detected at design time but shall be addressed at runtime. We observe that many faults (70%) are intrinsically hard to detect at design-time

E-MDAV: A Framework for Developing Data-Intensive Web Applications

The ever-increasing adoption of innovative technologies, such as big data and cloud computing, provides significant opportunities for organizations operating in the IT domain, but also introduces considerable challenges. Such innovations call for development processes that better align with stakeholders needs and expectations. In this respect, this paper introduces a development framework based on the OMG's Model Driven Architecture (MDA) that aims to support the development lifecycle of data-intensive web applications. The proposed framework, named E-MDAV (Extended MDA-VIEW), defines a methodology that exploits a chain of model transformations to effectively cope with both forward- and reverse-engineering aspects. In addition, E-MDAV includes the specification of a reference architecture for driving the implementation of a tool that supports the various professional roles involved in the development and maintenance of data-intensive web applications. In order to evaluate the effectiveness of the proposed E-MDAV framework, a tool prototype has been developed. E-MDAV has then been applied to two different application scenarios and the obtained results have been compared with historical data related to the implementation of similar development projects, in order to measure and discuss the benefits of the proposed approach