arXiv subjects

Rui Abreu

Publications and source records attributed to Rui Abreu.

41 records · Page 3Linked to original sources

arXiv2019

Catalog of Energy Patterns for Mobile Applications

Software engineers make use of design patterns for reasons that range from performance to code comprehensibility. Several design patterns capturing the body of knowledge of best practices have been proposed in the past, namely creational, structural and behavioral patterns. However, with the advent of mobile devices, it becomes a necessity a catalog of design patterns for energy efficiency. In this work, we inspect commits, issues and pull requests of 1027 Android and 756 iOS apps to identify common practices when improving energy efficiency. This analysis yielded a catalog, available online, with 22 design patterns related to improving the energy efficiency of mobile apps. We argue that this catalog might be of relevance to other domains such as Cyber-Physical Systems and Internet of Things. As a side contribution, an analysis of the differences between Android and iOS devices shows that the Android community is more energy-aware.

cs.SE↗

arXiv2018

Using Automatic Refactoring to Improve Energy Efficiency of Android Apps

The ever-growing popularity of mobile phones has brought additional challenges to the software development lifecycle. Mobile applications (apps, for short) ought to provide the same set of features as conventional software, with limited resources: such as, limited processing capabilities, storage, screen and, not less important, power source. Although energy efficiency is a valuable requirement, developers often lack knowledge of best practices. In this paper, we study whether or not automatic refactoring can aid developers ship energy efficient apps. We leverage a tool, Leafactor, with five energy code smells that tend to go unnoticed. We use Leafactor to analyze code smells in 140 free and open source apps. As a result, we detected and fixed code smells in 45 apps, from which 40% have successfully merged our changes into the official repository.

cs.SE↗

arXiv2017

Recognizing Abnormal Heart Sounds Using Deep Learning

The work presented here applies deep learning to the task of automated cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We describe an automated heart sound classification algorithm that combines the use of time-frequency heat map representations with a deep convolutional neural network (CNN). Given the cost-sensitive nature of misclassification, our CNN architecture is trained using a modified loss function that directly optimizes the trade-off between sensitivity and specificity. We evaluated our algorithm at the 2016 PhysioNet Computing in Cardiology challenge where the objective was to accurately classify normal and abnormal heart sounds from single, short, potentially noisy recordings. Our entry to the challenge achieved a final specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved the greatest specificity score out of all challenge entries and, using just a single CNN, our algorithm differed in overall score by only 0.02 compared to the top place finisher, which used an ensemble approach.

cs.SD↗

arXiv2015

Fault Detection in C Programs using Monitoring of Range Values: Preliminary Results

This technical report presents the work done as part of the AutoSeer project. Our work in this project was to develop a source-to-source compiler, MANET, for the C language that could be used for instrumentation of critical parts of applications under testing. The intention was to guide the compilation flow and define instrumentation strategies using the Aspect-Oriented Approach provided by LARA. This allows a separation of the original target application and the instrumentation secondary concerns. One of the goals of this work was the development of a source-to-source C compiler that modifies code according to an input strategy. These modifications could provide code transformations that target performance and instrumentation for debugging, but in this work they are used to inject code that collects information about the values that certain variables take during runtime. This compiler is supported by an AOP approach that enables the definition of instrumentation strategies. We decided to extend an existing source-to-source compiler, Cetus, and couple it with LARA, an AOP language that is partially abstracted from the target programming language. We propose and evaluate an approach to detect faults in C programs by monitoring the range values of variables. We consider various monitoring strategies and use two real-life applications, the GZIP file compressor and ABS, a program provided by an industrial partner. The different strategies were specified in LARA and automatically applied using MANET. The experimental results show that our approach has potential but is hindered by not accounting for values in arrays and control variables. We achieve prediction accuracies of around 54% for ABS and 83% for GZIP, when comparing our approach to a more traditional one, where the outputs are compared to an expected result.

cs.SE↗

arXiv2006

Program Spectra Analysis in Embedded Software: A Case Study

Because of constraints imposed by the market, embedded software in consumer electronics is almost inevitably shipped with faults and the goal is just to reduce the inherent unreliability to an acceptable level before a product has to be released. Automatic fault diagnosis is a valuable tool to capture software faults without extra effort spent on testing. Apart from a debugging aid at design and integration time, fault diagnosis can help analyzing problems during operation, which allows for more accurate system recovery. In this paper we discuss perspectives and limitations for applying a particular fault diagnosis technique, namely the analysis of program spectra, in the area of embedded software in consumer electronics devices. We illustrate these by our first experience with a test case from industry.

cs.SE↗