Rediscovery Datasets: Connecting Duplicate Reports

The same defect can be rediscovered by multiple clients, causing unplanned outages and leading to reduced customer satisfaction. In the case of popular open source software, high volume of defects is reported on a regular basis. A large number of these reports are actually duplicates / rediscoveries of each other. Researchers have analyzed the factors related to the content of duplicate defect reports in the past. However, some of the other potentially important factors, such as the inter-relationships among duplicate defect reports, are not readily available in defect tracking systems such as Bugzilla. This information may speed up bug fixing, enable efficient triaging, improve customer profiles, etc. In this paper, we present three defect rediscovery datasets mined from Bugzilla. The datasets capture data for three groups of open source software projects: Apache, Eclipse, and KDE. The datasets contain information about approximately 914 thousands of defect reports over a period of 18 years (1999-2017) to capture the inter-relationships among duplicate defects. We believe that sharing these data with the community will help researchers and practitioners to better understand the nature of defect rediscovery and enhance the analysis of defect reports

Recognizing Human Affection: Smartphone Perspective

Touch-screen Smartphone has become an obligatory segment in the lives of billions of people around the world. Understanding the human affection or emotional state of the user enables efficient human computer interaction. Smartphone is one of the most frequently used electronic devices and the number of applications developed for it is increasing day by day. Emotion recognition of the user will lead to the development of emotion aware applications. Service recommendations and intelligent user interfaces in Smartphone will be other encouraging scopes for the mobile application developers. In this paper we discuss about state-of-the-art technologies to detect human emotional states. We proposed a methodology by which three different emotional states (positive, neutral, negative) of the user can be identified using Smartphone2019;s built-in sensors like the gyroscope, accelerometer and also additional sensors such as pressure sensor. We tried to analyse infraction log of Smartphone users, approximated different sensor values to recognize human emotions. Since the pressure values found on the existing phones are not completely accurate, we introduced the use of Force Sensitive Resistor (FSR) sensor to get more accurate pressure values

Rediscovery Datasets: Connecting Duplicate Reports of LibreOffice and Gentoo

We present two defect rediscovery datasets mined from Bugzilla (supplementary to previously published datasets: Zenodo, http://doi.org/10.5281/zenodo.400614). These datasets capture data for two groups of open source software projects: LibreOffice and Gentoo. The datasets contain information about the inter-relationships among duplicate defects. File Descriptions libreoffice.csv - LibreOffice Defect Rediscovery dataset gentoo.csv - Gentoo Defect Rediscovery dataset libreoffice.relations.csv - Inter-relations of rediscovered defects of LibreOffice gentoo.relations.csv - Inter-relations of rediscovered defects of Gento