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A kernel or mini-app is a self-contained small application that retains certain characteristics of the original application [7]. Working on a kernel or mini-app in the place of the original application can dramatically reduce the resources and effort required for performing software tasks such as performance optimization and porting to new platforms. However, using kernel as a proxy is based on the...
Nowadays, more and more sources (connected devices, social networks, etc.) emit real-time data with fluctuating rates over time. Existing distributed stream processing engines (SPE) have to resolve a difficult problem: deliver results satisfying end-users in terms of quality and latency without over-consuming resources. This paper focuses on parallelization of operators to adapt their throughput to...
In this work, we report a study carried out to identify a set of metrics to early estimate the development effort of mobile apps. The applied methodology was inspired by the work of Mendes et al. who addressed a similar problem in the field of web apps. In particular, we extracted an initial set of metrics by analyzing the online quotes forms that companies made available on their websites. Afterward,...
Semi-supervised clustering has been widely explored in the last years. In this paper, we present HCAC-ML (Hierarchical Confidence-based Active Clustering with Metric Learning), an innovative approach for this task which employs distance metric learning through cluster-level constraints. HCAC-ML is based on the HCAC algorithm, an state-of-the-art algorithm for hierarchical semi-supervised clustering...
This technical briefing provides an overview of how quantitative empirical research methods can be combined with qualitative ones generating the family of empirical software engineering approaches known as mixed-methods. The ultimate aim of such mixed-methods is supporting cause-effect claims combining multiple data types, sources and analyses that provide software practitioners and academicians solid...
In recent years, there has been an increasing interest in music generation using machine learning techniques typically used for classification or regression tasks. This is a field still in its infancy, and most attempts are still characterized by the imposition of many restrictions to the music composition process in order to favor the creation of “interesting” outputs. Furthermore, and most importantly,...
Word embedding in the NLP area has attracted increasing attention in recent years. The continuous bag-of-words model (CBOW) and the continuous Skip-gram model (Skip-gram) have been developed to learn distributed representations of words from a large amount of unlabeled text data. In this paper, we explore the idea of integrating extra knowledge to the CBOW and Skip-gram models and applying the new...
While there is an increased appreciation for integrating haptic feedback with audio-visual content, there is still a lack of understanding of how to quantify the added value of touch for a user's experience (UX) of multimedia content. Here we focus on three main concepts to measure this added value: UX, emotions, and expectations. We present a case study measuring the added value of haptic feedback...
Techniques known as Nonlinear Set Membership prediction, Kinky Inference or Lipschitz Interpolation are fast and numerically robust approaches to nonparametric machine learning that have been proposed to be utilised in the context of system identification and learning-based control. They utilise presupposed Lipschitz properties in order to compute inferences over unobserved function values. Unfortunately,...
Test case maintainability is an important concern, especially in open source and distributed development environments where projects typically have high contributor turn-over with varying backgrounds and experience, and where code ownership changes often. Similar to design patterns, patterns for unit testing promote maintainability quality attributes such as ease of diagnoses, modifiability, and comprehension...
Managing variability is a hard task for every technique that develops variability-rich systems, such as software product lines (SPL), especially in its evolution. Hence, to be effective a technique should provide stability and respect the Open-Closed principle. Among the techniques to develop SPLs, delta-oriented programming (DOP) seems to be promising given its flexibility. There are two strategies...
The Error Bounded Exact BDD Minimization (EBEBM) problem arises in approximate computing when one is trying to find a functional approximation with a minimal representation in terms of BDD size for a single output function with respect to a given error bound. In this paper we present an exact algorithm for EBEBM. This algorithm constructs a BDD representing all functions, which meet the restrictions...
Context: Comparative study of software development methodologies in millenials high school students. Objective: This paper compares the performance and satisfaction of both students and teachers in using two development strategies in a K-12 Computer Science teaching practice. Method: This study includes an experiment, administered in a laboratory controlled setting to measure students' performances...
There is no metric that determines how well the implementation of a ticket has been tested. As a consequence, code changed within the context of a ticket might unintentionally remain untested and get into production. This is a major problem, because changed code is more fault-prone than unchanged code. In this paper, we introduce the metric ticket coverage which puts test coverage into the context...
Background: Test quality is a prerequisite for achieving production system quality. While the concept of quality is multidimensional, most of the effort in testing context hasbeen channelled towards measuring test effectiveness. Objective: While effectiveness of tests is certainly important, we aim to identify a core list of testing principles that also address other quality facets of testing, and...
Testing and debugging automotive cyber physical systems are challenging. Developing and integrating cyber and physical components require extensive testing to ensure reliable and safe releases. One important cost factor in the debugging process is the time required to analyze failures. Since large number of failures usually happen due to a few underlying faults, clustering failures based on the responsible...
Diagnosing problems in large-scale, distributed applications runningin cloud environments requires investigating different sources ofinformation to reason about application state at any given time. Typical sources of information available to developers and operatorsinclude log statements and other runtime information collectedby monitors, such as application and system metrics. Just as importantly,...
Hiring is one of the important challenges in the context of online labor marketplace. Unlike traditional hiring, where workers are hired either as a full time employee or as a contractor, hiring from online marketplaces are done for individual jobs of short duration. As these marketplaces are open for anyone, hiring becomes challenging due to the large number of freelancers applying for a posted job...
Traceability Link Recovery (TLR) is a fundamental software maintenance task in which links are established between related software artifacts of different types (e.g., source code, documentation, requirements specifications, etc.) within a system. Existing approaches to TLR often require a human to analyze a long list of potential links and distinguish valid links from invalid ones. Here we present...
We propose to study the impact of the representation of the data in defect prediction models. For this study, we focus on the use of developer activity data, from which we structure dependency graphs. Then, instead of manually generating features, such as network metrics, we propose a model inspired in recent advances in Representation Learning which are able to automatically learn representations...
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