The Design and Implement of Open Source License Tracking System

2010 ◽  
Author(s):  
HongBo Xu ◽  
HuiHui Yang ◽  
Dan Wan ◽  
JiangPing Wan
Keyword(s):  
Open Source ◽  
Tracking System ◽  
Open Source License

scholarly journals DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning

2020 ◽  
Author(s):  
Wei Hao Khoong
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Preprocessing ◽  
Weighted Distance ◽  
Open Source License ◽  
Classification Tasks ◽  
Python Package

In this paper, we introduce deboost, a Python library devoted to weighted distance ensembling of predictions for regression and classification tasks. Its backbone resides on the scikit-learn library for default models and data preprocessing functions. It offers flexible choices of models for the ensemble as long as they contain the predict method, like the models available from scikit-learn. deboost is released under the MIT open-source license and can be downloaded from the Python Package Index (PyPI) at https://pypi.org/project/deboost. The source scripts are also available on a GitHub repository at https://github.com/weihao94/DEBoost.

scholarly journals MINING EFFORT DATA FROM THE OSS REPOSITORY OF DEVELOPER’S BUG FIX ACTIVITY

2016 ◽  
Vol 3 (1) ◽  
pp. 107-128
Author(s):  
Syed Nadeem Ahsan ◽  
Muhammad Tanvir Afzal ◽  
Safdar Zaman ◽  
Christian Gütel ◽  
Franz Wotawa
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
State Of The Art ◽  
Tracking System ◽  
Previous History ◽  
Effort Estimation ◽  
Estimation Model ◽  
Activity Data ◽  
Actual Time ◽  
History Of

During the evolution of any software, efforts are made to fix bugs or to add new features in software. In software engineering, previous history of effort data is required to build an effort estimation model, which estimates the cost and complexity of any software. Therefore, the role of effort data is indispensable to build state-of-the-art effort estimation models. Most of the Open Source Software does not maintain any effort related information. Consequently there is no state-of-the-art effort estimation model for Open Source Software, whereas most of the existing effort models are for commercial software. In this paper we present an approach to build an effort estimation model for Open Source Software. For this purpose we suggest to mine effort data from the history of the developer’s bug fix activities. Our approach determines the actual time spend to fix a bug, and considers it as an estimated effort. Initially, we use the developer’s bug-fix-activity data to construct the developer’s activity log-book. The log-book is used to store the actual time elapsed to fix a bug. Subsequently, the log-book information is used to mine the bug fix effort data. Furthermore, the developer’s bug fix activity data is used to define three different measures for the developer’s contribution or expertise level. Finally, we used the bug-fix-activity data to visualize the developer’s collaborations and the involved source files. In order to perform an experiment we selected the Mozilla open source project and downloaded 93,607 bug reports from the Mozilla project bug tracking system i.e., Bugzilla. We also downloaded the available CVS-log data from the Mozilla project repository. In this study we reveal that in case of Mozilla only 4.9% developers have been involved in fixing 71.5% of the reported bugs.

Using R

2020 ◽  
pp. 3-39
Author(s):  
Bendix Carstensen
Keyword(s):  
Open Source ◽  
Epidemiological Studies ◽  
Commercial Product ◽  
Frequency Data ◽  
Open Source License ◽  
Free Open Source

This chapter discusses how the best way to learn R is to use it. One should start by using it as a simple calculator, and keep on exploring what one gets back by inspecting the size, shape, and content of what one creates. R is available from CRAN, the Comprehensive R Archive Network. A nice interface to R is RStudio, which is a commercial product, but RStudio has a free open source license that allows one to have a very good and handy interface to R for free, including the possibility of writing reports using Rmarkdown, Sweave, or knitr. The chapter then looks at the two main graphics systems used in R: base graphics, which is an integral part of any R distribution, and ggplot2 (gg referring to grammar of graphics). Data from large epidemiological studies are often summarized in the form of frequency data, which record the frequency of all possible combinations of values of the variables in the study.

From Android Bug Reports to Android Bug Handling Process

2016 ◽  
Vol 7 (4) ◽  
pp. 1-18
Author(s):  
Liguo Yu
Keyword(s):  
Project Management ◽  
Open Source ◽  
Tracking System ◽  
Software Project ◽  
Bug Reports ◽  
Bug Report ◽  
Bug Tracking ◽  
Android Development ◽  
Comprehensive Study

Android is an operating system for mobile devices. Its development is led by Google and some other companies. Because of the open-source property of Android, anyone can report a bug through its online bug tracking system. In this paper, we analyze the bug reports of Android operating systems. Specifically, through this study, we would like to answer the following questions regarding Android development and its project management: (1) Could Android bug reports be handled on time? (2) What is the distribution of different maintenance activities initiated by Android bug reports? (3) How long does it take to handle an Android bug report? (4) Are the number of followers and the number of following messages of an Android bug report related to the effort spent on handling this bug report? Through answering these questions, this paper presents a comprehensive study of Android bug reporting and handling process. The information and knowledge obtained through this case study could help us better understand open-source software project, such as its development process and project management.

Bug Fixing Practices within Free/Libre Open Source Software Development Teams

2009 ◽  
pp. 1079-1110 ◽  
Author(s):  
Kevin Crowston ◽  
Barbara Scozzi
Keyword(s):  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Tracking System ◽  
Distributed Teams ◽  
Distributed Software ◽  
Bug Fixing ◽  
Development Teams ◽  
Bulletin Boards ◽  
Software Development Teams

Free/Libre open source software (FLOSS, e.g., Linux or Apache) is primarily developed by distributed teams. Developers contribute from around the world and coordinate their activity almost exclusively by means of email and bulletin boards, yet some how profit from the advantages and evade the challenges of distributed software development. In this article we investigate the structure and the coordination practices adopted by development teams during the bug-fixing process, which is considered one of main areas of FLOSS project success. In particular, based on a codification of the messages recorded in the bug tracking system of four projects, we identify the accomplished tasks, the adopted coordination mechanisms, and the role undertaken by both the FLOSS development team and the FLOSS community. We conclude with suggestions for further research.

Bug Fixing Practices within Free/Libre Open Source Software Development Teams

2009 ◽  
pp. 797-828
Author(s):  
Kevin Crowston ◽  
Barbara Scozzi
Keyword(s):  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Tracking System ◽  
Distributed Teams ◽  
Distributed Software ◽  
Bug Fixing ◽  
Development Teams ◽  
Bulletin Boards ◽  
Software Development Teams

Free/Libre open source software (FLOSS, e.g., Linux or Apache) is primarily developed by distributed teams. Developers contribute from around the world and coordinate their activity almost exclusively by means of email and bulletin boards, yet some how profit from the advantages and evade the challenges of distributed software development. In this article we investigate the structure and the coordination practices adopted by development teams during the bug-fixing process, which is considered one of main areas of FLOSS project success. In particular, based on a codification of the messages recorded in the bug tracking system of four projects, we identify the accomplished tasks, the adopted coordination mechanisms, and the role undertaken by both the FLOSS development team and the FLOSS community. We conclude with suggestions for further research.

scholarly journals Balance Trees Reveal Microbial Niche Differentiation

mSystems ◽  
2017 ◽  
Vol 2 (1) ◽  
Author(s):  
James T. Morton ◽  
Jon Sanders ◽  
Robert A. Quinn ◽  
Daniel McDonald ◽  
Antonio Gonzalez ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Open Source ◽  
Niche Differentiation ◽  
Difficult Problem ◽  
Individual Species ◽  
Rrna Gene ◽  
Link Type ◽  
Open Source License ◽  
Gene Data

ABSTRACT By explicitly accounting for the compositional nature of 16S rRNA gene data through the concept of balances, balance trees yield novel biological insights into niche differentiation. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/gneiss . Advances in sequencing technologies have enabled novel insights into microbial niche differentiation, from analyzing environmental samples to understanding human diseases and informing dietary studies. However, identifying the microbial taxa that differentiate these samples can be challenging. These issues stem from the compositional nature of 16S rRNA gene data (or, more generally, taxon or functional gene data); the changes in the relative abundance of one taxon influence the apparent abundances of the others. Here we acknowledge that inferring properties of individual bacteria is a difficult problem and instead introduce the concept of balances to infer meaningful properties of subcommunities, rather than properties of individual species. We show that balances can yield insights about niche differentiation across multiple microbial environments, including soil environments and lung sputum. These techniques have the potential to reshape how we carry out future ecological analyses aimed at revealing differences in relative taxonomic abundances across different samples. IMPORTANCE By explicitly accounting for the compositional nature of 16S rRNA gene data through the concept of balances, balance trees yield novel biological insights into niche differentiation. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/gneiss . Author Video: An author video summary of this article is available.

A Method of Statistical Process Control for Successful Open Source Software Projects and Its Application to Determining the Development Period

2016 ◽  
Vol 23 (05) ◽  
pp. 1650018 ◽  
Author(s):  
Shigeru Yamada ◽  
Masakazu Yamaguchi
Keyword(s):  
Process Control ◽  
Open Source ◽  
Statistical Process Control ◽  
Open Source Software ◽  
Execution Time ◽  
Tracking System ◽  
Time Model ◽  
Statistical Process ◽  
Software Release ◽  
Bug Tracking

A software development paradigm for open source software (OSS) project has been rapidly spread in recent years. On the other hand, an effective method of quality management has not been established due to the unique development characteristics such as no testing phase. In this paper, we assume that the number of fault-detections observed on the bug tracking system tends to infinity, and discuss a method of statistical process control (SPC) for OSS projects by applying the logarithmic Poisson execution time model as a software reliability growth model (SRGM) based on a nonhomogeneous Poisson process (NHPP). Then, we propose a control chart method based on the logarithmic Poisson execution time model for judging the statical stability state, and estimating the additional development time for attaining the objective software failure intensity, i.e., the target value of the instantaneous fault-detection rate per unit time. We also discuss an optimal software release problem for determining the optimum time when to stop OSS development and to transfer it to user operation. Further, numerical illustrations for SPC are shown by applying the actual fault-count data observed on the bug tracking system.

scholarly journals Cricket umpire assistance and ball tracking system using a single smartphone camera

2017 ◽  
Author(s):  
Udit Arora ◽  
Sohit Verma ◽  
Sarthak Sahni ◽  
Tushar Sharma
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Motion Tracking ◽  
Tracking System ◽  
Contour Detection ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ball Tracking ◽  
Histogram Of Gradients

Several ball tracking algorithms have been reported in literature. However, most of them use high-quality video and multiple cameras, and the emphasis has been on coordinating the cameras or visualizing the tracking results. This paper aims to develop a system for assisting the umpire in the sport of Cricket in making decisions like detection of no-balls, wide-balls, leg before wicket and bouncers, with the help of a single smartphone camera. It involves the implementation of Computer Vision algorithms for object detection and motion tracking, as well as the integration of machine learning algorithms to optimize the results. Techniques like Histogram of Gradients (HOG) and Support Vector Machine (SVM) are used for object classification and recognition. Frame subtraction, minimum enclosing circle, and contour detection algorithms are optimized and used for the detection of a cricket ball. These algorithms are applied using the Open Source Python Library - OpenCV. Machine Learning techniques - Linear and Quadratic Regression are used to track and predict the motion of the ball. It also involves the use of open source Python library VPython for the visual representation of the results. The paper describes the design and structure for the approach undertaken in the system for analyzing and visualizing off-air low-quality cricket videos.

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