Reclassifying Success and Tragedy in FLOSS Projects

•Descargar como PPT, PDF•

0 recomendaciones•478 vistas

Andrea Wiggins

Desarrollo personal

Motivation ,[object Object],[object Object],[object Object]

Success and Tragedy ,[object Object],[object Object],[object Object],[object Object]

Replication ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Extended Analysis: Suggested Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Extended Analysis: Over Time ,[object Object],[object Object],[object Object],[object Object]

Results ,[object Object],[object Object],[object Object]

Comparison to Original Results 117,733 (+9.6%) 119,355 Total n/a 8,422 other 16,045 (14%) 15,782 (15%) SG 32,642 (28%) 30,592 (28%) TG 36,507 (31%) 37,320 (35%) TI 16,252 (14%) 10,711 (10%) IG 16,252 (16%) 13,342 (12%) II 3,296 3,186 unclassifiable Replication Original 2006-10

Comparison of Release Rate Criteria 32% 32% 32% TI 33% 27% 28% TG 3% 13% 13% SG 14% 14% 14% II 16% 12% 11% IG Method 3* Method 2 Method 1 2006-10-23

Comparison Over Time 117,733 112,430 Total 3,296 (2.8%) 3,343 (3.0%) unclassifiable 36,507 (31.0%) 39,948 (35.5%) TI 32,642 (27.7%) 28,777 (25.6%) TG 16,045 (13.6%) 14,244 (12.7%) SG 16,252 (13.8%) 13,592 (12.4%) II 12,991 (11.0%) 12,166 (10.8%) IG 2006-10-23 2006-04-21 Class

Discussion of Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Limitations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object]

Conclusions ,[object Object],[object Object],[object Object],[object Object]

Questions? ,[object Object],[object Object]

Más contenido relacionado

Similar a Reclassifying Success and Tragedy in FLOSS Projects

Replicating FLOSS Research as eResearch

Andrea Wiggins

sers, Applications and the Community of Practice for the Air Quality Scenario

Rudolf Husar

2008-05-05 GEOSS UIC-ADC AQ Scen W shop Toronto

Rudolf Husar

The IUPAC Solubility Data Series published its first volume in 1979. Since then over 100 volumes of high quality peer reviewed solubility data has been published, first in hardcopy and subsequently electronically as part of the Journal of Physical and Chemistry Reference Data. In February of this year the National Institute of Standards and Technology (NIST) funded a grant to explore taking the 18 currently available online volumes of data and re-purpose them as a REST based website, with documented API, and semantic representation/annotation. In this way the high quality data from these volumes can be shared, both to humans and computers. In addition, the semantic representation of the data allows integration of the data with other semantically enabled data at repositories across the globe. This presentation will give an overview of the process of schema development for the dataset, implementation in MySQL, website construction in the CakePHP framework, and architecture of the API access points. A report on the ontology development to support the project will also be discussed.

Sharing Science Data: Semantically Reimagining the IUPAC Solubility Series Data

Stuart Chalk

Dats nih-dccpc-kc7-april2018-prs-uoxf

Philippe Rocca-Serra

Replication and Benchmarking in Software Analytics

University of Zurich

This tutorial was held at IEEE BigData '14 on October 29, 2014 in Bethesda, ML, USA. Presenters: Chaitan Baru and Tilmann Rabl More information available at: http://msrg.org/papers/BigData14-Rabl Summary: This tutorial will introduce the audience to the broad set of issues involved in defining big data benchmarks, for creating auditable industry-standard benchmarks that consider performance as well as price/performance. Big data benchmarks must capture the essential characteristics of big data applications and systems, including heterogeneous data, e.g. structured, semi- structured, unstructured, graphs, and streams; large-scale and evolving system configurations; varying system loads; processing pipelines that progressively transform data; workloads that include queries as well as data mining and machine learning operations and algorithms. Different benchmarking approaches will be introduced, from micro-benchmarks to application- level benchmarking. Since May 2012, five workshops have been held on Big Data Benchmarking including participation from industry and academia. One of the outcomes of these meetings has been the creation of industry’s first big data benchmark, viz., TPCx-HS, the Transaction Processing Performance Council’s benchmark for Hadoop Systems. During these workshops, a number of other proposals have been put forward for more comprehensive big data benchmarking. The tutorial will present and discuss salient points and essential features of such benchmarks that have been identified in these meetings, by experts in big data as well as benchmarking. Two key approaches are now being pursued—one, called BigBench, is based on extending the TPC- Decision Support (TPC-DS) benchmark with big data applications characteristics. The other called Deep Analytics Pipeline, is based on modeling processing that is routinely encountered in real-life big data applications. Both will be discussed. We conclude with a discussion of a number of future directions for big data benchmarking

Big Data Benchmarking Tutorial

Tilmann Rabl

Centerpoint posts hundreds of versions per day coming from a large base of editors. This session covers an implementation of SSP All Edits technology to capture, search, and visualize attribute and geometry edits in each of the versions long after the version has been posted. Centerpoint can search for edits across the versions by work order and will keep the history of posted edits indefinitely using a new archival process. See the power of visualizing the who, what, and where of your edits!

Enterprise Audit Tracking at CenterPoint Energy, Show Me the Edits!

SSP Innovations

Data Collection Points And Gqm

Gerrit Klaschke, CSM

Provinance in scientific workflows in e science

bdemchak

Collaborative Data Analysis with Taverna Workflows

Andrea Wiggins

This is a research presentation by Aparna Varde during a summer research visit at the Max Planck Institute for Informatics, Saarbruecken, Germany in August 2015 within the research group of Dr. Gerhard Weikum, The presentation focuses on various aspects of data mining and knowledge discovery pertinent to environmental science and management. It encompasses three main topics: (1) decision support for the greening of data centers; (2) predictive analysis in urban planning and simulation; and (3) common sense knowledge for domain-specific KBs. It includes a few brief highlights on web and text mining in article / collocation error detection as well as in terminology evolution. This presentation is based on her relevant work as per August 2015, serving as an invited talk during this research visit.

Knowledge Discovery in Environmental Management

Dr. Aparna Varde

The clinical development data deluge is reaching critical mass for pharmaceuticals. Use of varied data for targeted outcomes remains difficult, despite studies that generate evidence of the risk-benefit profile of investigational products. New technologies are federating the ability to leverage analytic-ready data for innovations in clinical operations and clinical science. With the application of clinical data-as-a-service and meta-data core, centralized clinical data lakes have the power to improve data quality, evidence generation, and time-to-insights. Karim Damji and Benzi Mathews presented this deck at the Clinical Trial Innovation Summit held in Boston on April 24-26.

Next Gen Clinical Data Sciences

Saama

WSDM 2011 - Nicolaas Matthijs and Filip Radlinski

Nicolaas Matthijs

20191219 finding articles for your research

yunhee shim

Foss4g evolution-gis-data

Joachim Van der Auwera

This slide show describes the difficulties in implementing Test-Driven Development (TDD) in the context of analytics and data engineering in development and maintenance phases. If we assumes that the objective of TDD is to reduce cycle time, improve developer productivity and improve production quality. It identifies 7 challenges from the analytics literature and a further 10 from interviews (n=14) and survey respondents (n=20) selected from analytics leaders. A key theme emerging as an output is that many of the challenges can be addressed through education and coaching, notably around data literacy for key stakeholders and executives

Why is TDD so hard for Data Engineering and Analytics Projects?

Phil Watt

Data analytics in computer networking

Stenio Fernandes

Software project estimation is important for allocating resources and planning a reasonable work schedule. Estimation models are typically built using data from completed projects. While organizations have their historical data repositories, it is difficult to obtain their collaboration due to privacy and competitive concerns. To overcome the issue of public access to private data repositories this study proposes an algorithm to extract sufficient data from the GitHub repository for building duration estimation models. More specifically, this study extracts and analyses historical data on WordPress projects to estimate OSS project duration using commits as an independent variable as well as an improved classification of contributors based on the number of active days for each contributor within a release period. The results indicate that duration estimation models using data from OSS repositories perform well and partially solves the problem of lack of data encountered in empirical research in software engineering.

A DATA EXTRACTION ALGORITHM FROM OPEN SOURCE SOFTWARE PROJECT REPOSITORIES FO...

ijseajournal

A DATA EXTRACTION ALGORITHM FROM OPEN SOURCE SOFTWARE PROJECT REPOSITORIES FO...

ijseajournal

Similar a Reclassifying Success and Tragedy in FLOSS Projects (20)

Replicating FLOSS Research as eResearch

sers, Applications and the Community of Practice for the Air Quality Scenario

2008-05-05 GEOSS UIC-ADC AQ Scen W shop Toronto

Sharing Science Data: Semantically Reimagining the IUPAC Solubility Series Data

Dats nih-dccpc-kc7-april2018-prs-uoxf

Replication and Benchmarking in Software Analytics

Big Data Benchmarking Tutorial

Enterprise Audit Tracking at CenterPoint Energy, Show Me the Edits!

Data Collection Points And Gqm

Provinance in scientific workflows in e science

Collaborative Data Analysis with Taverna Workflows

Knowledge Discovery in Environmental Management

Next Gen Clinical Data Sciences

WSDM 2011 - Nicolaas Matthijs and Filip Radlinski

20191219 finding articles for your research

Foss4g evolution-gis-data

Why is TDD so hard for Data Engineering and Analytics Projects?

Data analytics in computer networking

A DATA EXTRACTION ALGORITHM FROM OPEN SOURCE SOFTWARE PROJECT REPOSITORIES FO...

Más de Andrea Wiggins

Citizen Science Phenotypes

Andrea Wiggins

Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Sc...

Andrea Wiggins

With Great Data Comes Great Responsibility

Andrea Wiggins

Crowdsourcing Scientific Work: A Comparative Study of Technologies, Processes...

Andrea Wiggins

Mechanisms for Data Quality and Validation in Citizen Science

Andrea Wiggins

Open Source & Citizen Science

Andrea Wiggins

From Conservation to Crowdsourcing: A Typology of Citizen Science

Andrea Wiggins

Motivation by Design: Technologies, Experiences, and Incentives

Andrea Wiggins

Data Intensive Collaboration in Science and Engineering: CSCW workshop themes

Andrea Wiggins

Secondary data analysis with digital trace data

Andrea Wiggins

Open Source, Open Science, & Citizen Science

Andrea Wiggins

Crowdsourcing Science

Andrea Wiggins

Intellectual Diversity in the iSchools: Past, Present and Future

Andrea Wiggins

Distributed Scientific Collaboration: Research Opportunities in Citizen Science

Andrea Wiggins

Designing Virtual Organizations for Citizen Science

Andrea Wiggins

National Park System Property Designations

Andrea Wiggins

Tales of the Field: Building Small Science Cyberinfrastructure

Andrea Wiggins

Little eScience

Andrea Wiggins

Coordination Dynamics in Free/Libre and Open Source Software

Andrea Wiggins

Heartbeat: Measuring Active User Base and Potential User Interest

Andrea Wiggins

Más de Andrea Wiggins (20)

Citizen Science Phenotypes

Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Sc...

With Great Data Comes Great Responsibility

Crowdsourcing Scientific Work: A Comparative Study of Technologies, Processes...

Mechanisms for Data Quality and Validation in Citizen Science

Open Source & Citizen Science

From Conservation to Crowdsourcing: A Typology of Citizen Science

Motivation by Design: Technologies, Experiences, and Incentives

Data Intensive Collaboration in Science and Engineering: CSCW workshop themes

Secondary data analysis with digital trace data

Open Source, Open Science, & Citizen Science

Crowdsourcing Science

Intellectual Diversity in the iSchools: Past, Present and Future

Distributed Scientific Collaboration: Research Opportunities in Citizen Science

Designing Virtual Organizations for Citizen Science

National Park System Property Designations

Tales of the Field: Building Small Science Cyberinfrastructure

Little eScience

Coordination Dynamics in Free/Libre and Open Source Software

Heartbeat: Measuring Active User Base and Potential User Interest

Último

2023 - Between Philosophy and Practice: Introducing Yoga

Raphaël Semeteys

March 2023 Recommendations for newsletter

ssuserdfec6a

Whatsapp Number Escorts Call girls 8617370543 Available 24x7 Dadar West Call Girls Service Offer Genuine VIP Model Escorts Call Girls in Your Budget. Dadar West Call Girls Service Provide Real Call Girls Number. Make Your Sexual Pleasure Memorable with Our Dadar West Call Girls at Affordable Price. Top VIP Escorts Call Girls, High Profile Independent Escorts Call Girls, Housewife Women Escorts Call Girl, College Girls Escorts Call Girls, Russian Escorts Call girls Service in Your Budget.

Dadar West Escorts 🥰 8617370543 Call Girls Offer VIP Hot Girls

Deepika Singh

KLINIK BATA Jual obat penggugur kandungan 087776558899 ABORSI JANIN KEHAMILAN...

Cara Menggugurkan Kandungan 087776558899

Booking Contact Details WhatsApp Chat: +91-7738596112 Moblie number:- +91-7738596112 Mumbai Escort Service includes providing maximum physical satisfaction to their clients as well as engaging conversation that keeps your time enjoyable and entertaining. Plus they look fabulously elegant;$S2 making an impressionable. VIP call girl and escort service Mumbai and Mumbai. Will conform to you and obey all your words. You can do everything with them to satisfy your sexual needs Independent Escorts Mumbai understands the value of confidentiality and discretion - they will go the extra mile to meet your needs. Simply contact them via text messaging or through their online profiles; they'd be more than delighted to accommodate any request or arrange a romantic date or fun-filled night together. We provide - Flexibility Choices and options Lists of many beauty fantasies Turn your dream into reality Perfect companionship Cheap and convenient In-call and Out-call services And many more.

Call Girls In Mumbai Just Genuine Call ☎ 7738596112✅ Call Girl Andheri East G...

mitaliverma221

WOMEN EMPOWERMENT women empowerment.pptx

padhand000

SIKP311 Sikolohiyang Pilipino - Ginhawa.pptx

StephenMino

February 2024 Recommendations for newsletter

ssuserdfec6a

Pokemon Go... Unraveling the Conspiracy Theory

drae5

the Husband rolesBrown Aesthetic Cute Group Project Presentation

brynpueblos04

Reclassifying Success and Tragedy in FLOSS Projects

1. Reclassifying Success and Tragedy in FLOSS Projects Andrea Wiggins Kevin Crowston 1 June, 2010

8. Comparison to Original Results 117,733 (+9.6%) 119,355 Total n/a 8,422 other 16,045 (14%) 15,782 (15%) SG 32,642 (28%) 30,592 (28%) TG 36,507 (31%) 37,320 (35%) TI 16,252 (14%) 10,711 (10%) IG 16,252 (16%) 13,342 (12%) II 3,296 3,186 unclassifiable Replication Original 2006-10

9. Comparison of Release Rate Criteria 32% 32% 32% TI 33% 27% 28% TG 3% 13% 13% SG 14% 14% 14% II 16% 12% 11% IG Method 3* Method 2 Method 1 2006-10-23

10. Comparison Over Time 117,733 112,430 Total 3,296 (2.8%) 3,343 (3.0%) unclassifiable 36,507 (31.0%) 39,948 (35.5%) TI 32,642 (27.7%) 28,777 (25.6%) TG 16,045 (13.6%) 14,244 (12.7%) SG 16,252 (13.8%) 13,592 (12.4%) II 12,991 (11.0%) 12,166 (10.8%) IG 2006-10-23 2006-04-21 Class

11. Changes to Project Classification

12. Changes to Project Classification

13.

14.

15.

16.

17.

Notas del editor

Shift in TI may be because the thresholds set for determining project failure are too low?

Reclassifying Success and Tragedy in FLOSS Projects

Recomendados

Recomendados

Más contenido relacionado

Similar a Reclassifying Success and Tragedy in FLOSS Projects

Similar a Reclassifying Success and Tragedy in FLOSS Projects (20)

Más de Andrea Wiggins

Más de Andrea Wiggins (20)

Último

Último (10)

Reclassifying Success and Tragedy in FLOSS Projects

Notas del editor