Fault-Proneness Estimation and Java Migration: A Preliminary Case Study

The paper presents and discusses an industrial case study, where an eight year running software project has been analyzed. We collected about 1000 daily-versions, together with the file version control system, and bug tracking data. This project has been migrated from Java 1.4 to Java 1.5, and visible effects of this migration on the bytecode are presented and discussed. From this case study, we expect to observe the effects on the code size produced by the Java technology migration, and to improve the performances of already existing fault-proneness estimation models. Preliminary results about fault-proneness estimation are shown

Overview on Trust in Large FLOSS Communities

Abstract. The paper presents a survey of mature Free/Libre Open Source Software communities. The main focus of the survey is the collection of data related to the practices of these communities related to trust elements in their products. The survey is carried out using a structured questionnaire about thoughts and practices followed by Free/Libre Open Source Software communities. The survey focuses on the analysis of the development processes adopted by such communities. The results of the survey confirms basic ideas related to Free/Libre Open Source Software and explains in more detail specific issues related to trust and trustworthiness of the Free/Libre Open Source Software development process

Size Matters: Microservices Research and Applications

In this chapter we offer an overview of microservices providing the introductory information that a reader should know before continuing reading this book. We introduce the idea of microservices and we discuss some of the current research challenges and real-life software applications where the microservice paradigm play a key role. We have identified a set of areas where both researcher and developer can propose new ideas and technical solutions.Comment: arXiv admin note: text overlap with arXiv:1706.0735

Analyzing Load Profiles of Energy Consumption to Infer Household Characteristics Using Smart Meters

The increasing penetration of smart meters provides an excellent opportunity to monitor and analyze energy consumption in residential buildings. In this paper, we propose a framework to process the observed profiles of energy consumption to infer the household characteristics in residential buildings. Such characteristics can be used for improving resource allocation and for an efficient energy management that will ultimately contribute to reducing carbon dioxide (CO 2 ) emission. Our approach is based on automated extraction of features from univariate time-series data and development of a model through a variant of the decision trees technique (i.e., ensemble learning mechanism) random forest. We process and analyzed energy consumption data to answer four primitive questions. To evaluate the approach, we performed experiments on publicly available datasets. Our experiments show a precision of 82% and a recall of 81% in inferring household characteristics