Cognitive-Support Code Review Tools
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Open Source Used in Influx1.8 Influx 1.9
Open Source Used In Influx1.8 Influx 1.9 Cisco Systems, Inc. www.cisco.com Cisco has more than 200 offices worldwide. Addresses, phone numbers, and fax numbers are listed on the Cisco website at www.cisco.com/go/offices. Text Part Number: 78EE117C99-1178791953 Open Source Used In Influx1.8 Influx 1.9 1 This document contains licenses and notices for open source software used in this product. With respect to the free/open source software listed in this document, if you have any questions or wish to receive a copy of any source code to which you may be entitled under the applicable free/open source license(s) (such as the GNU Lesser/General Public License), please contact us at [email protected]. In your requests please include the following reference number 78EE117C99-1178791953 Contents 1.1 golang-protobuf-extensions v1.0.1 1.1.1 Available under license 1.2 prometheus-client v0.2.0 1.2.1 Available under license 1.3 gopkg.in-asn1-ber v1.0.0-20170511165959-379148ca0225 1.3.1 Available under license 1.4 influxdata-raft-boltdb v0.0.0-20210323121340-465fcd3eb4d8 1.4.1 Available under license 1.5 fwd v1.1.1 1.5.1 Available under license 1.6 jaeger-client-go v2.23.0+incompatible 1.6.1 Available under license 1.7 golang-genproto v0.0.0-20210122163508-8081c04a3579 1.7.1 Available under license 1.8 influxdata-roaring v0.4.13-0.20180809181101-fc520f41fab6 1.8.1 Available under license 1.9 influxdata-flux v0.113.0 1.9.1 Available under license 1.10 apache-arrow-go-arrow v0.0.0-20200923215132-ac86123a3f01 1.10.1 Available under -
Buildbot Documentation Release 1.6.0
Buildbot Documentation Release 1.6.0 Brian Warner Nov 17, 2018 Contents 1 Buildbot Tutorial 3 1.1 First Run.................................................3 1.2 First Buildbot run with Docker......................................6 1.3 A Quick Tour...............................................9 1.4 Further Reading............................................. 17 2 Buildbot Manual 23 2.1 Introduction............................................... 23 2.2 Installation................................................ 29 2.3 Concepts................................................. 41 2.4 Secret Management........................................... 50 2.5 Configuration............................................... 53 2.6 Customization.............................................. 251 2.7 Command-line Tool........................................... 278 2.8 Resources................................................. 289 2.9 Optimization............................................... 289 2.10 Plugin Infrastructure in Buildbot..................................... 289 2.11 Deployment............................................... 290 2.12 Upgrading................................................ 292 3 Buildbot Development 305 3.1 Development Quick-start......................................... 305 3.2 General Documents........................................... 307 3.3 APIs................................................... 391 3.4 Python3 compatibility.......................................... 484 3.5 Classes................................................. -
Gerrit J.J. Van Den Burg, Phd London, UK | Email: [email protected] | Web: Gertjanvandenburg.Com
Gerrit J.J. van den Burg, PhD London, UK | Email: [email protected] | Web: gertjanvandenburg.com Summary I am a research scientist with a PhD in machine learning and 8+ years of experience in academic research. I am currently looking to transition to industry to work on complex, large-scale problems that can have a positive real-world impact. I have extensive experience with machine learning modeling, algorithm design, and software engineering in Python, C, and R. My goal is to use my expertise and technical skills to address ongoing research challenges in machine learning and AI. Research Experience Postdoctoral Researcher — The Alan Turing Institute, UK 2018–2021 • Introduced a memorization score for probabilistic deep generative models and showed that neural networks can remember part of their input data, which has important implications for data privacy • Created a method for structure detection in textual data files that improved on the Python builtin method by 21%. Developed this into a Python package that has received over 600,000 downloads • Developed a robust Bayesian matrix factorization algorithm for time series modeling and forecasting that improved imputation error up to 60% while maintaining competitive runtime • Established the first benchmark dataset for change point detection on general real-world time series and determined the best performing methods, with consequences for research and practice • Collaborated with other researchers to design a unified interface for AI-powered data cleaning tools Doctoral Researcher -
Optimization of Software Quality Using Management and Technical Review Techniques
International Journal of Computer Trends and Technology (IJCTT) – volume 17 Number 6 Nov 2014 Optimization of Software Quality using Management and Technical Review Techniques Inibehe Emmanuel Akpannah Post Graduate Student (MSc. Information Technology), SRM University, Chennai, India Abstract compromising the security of the entire system. Optimizing the quality of software is a function of Improving the quality of our software process leads the degree of reviews made during the early life of to minimal rework, cost effectiveness and also a software development process. Reviews detect meeting up with schedules, which all lead to errors and potential errors early in the software improvement in capability measurement. Software development process. The errors detected during review is a chain process, it is dynamic and review the early life cycle of software are least expensive needs to be performed at every phase of the to correct. Efficient involvement in software development process until there is a transition to the inspections and technical reviews, help developers testing phase. improve their own skills, thereby mitigating the Software design and code review is a phase in the occurrence of errors in the later stage of software software development process in which development process. The ideas gathered on this programmers (authors of codes), quality assurance paper point that a properly implemented program of (QA) team, and peer reviewers get together to technical and management reviews drastically review design and code [9]. One way of optimizing reduces the time as well as the cost required for the quality of software is finding and correcting testing, debugging, and reworking, and dramatically errors at the early stage of a software development improves the quality of the resulting product. -
The'as Code'activities: Development Anti-Patterns for Infrastructure As Code
Empirical Software Engineering manuscript No. (will be inserted by the editor) The `as Code' Activities: Development Anti-patterns for Infrastructure as Code Akond Rahman · Effat Farhana · Laurie Williams Pre-print accepted at the Empirical Software Engineering Journal Abstract Context: The `as code' suffix in infrastructure as code (IaC) refers to applying software engineering activities, such as version control, to main- tain IaC scripts. Without the application of these activities, defects that can have serious consequences may be introduced in IaC scripts. A systematic investigation of the development anti-patterns for IaC scripts can guide prac- titioners in identifying activities to avoid defects in IaC scripts. Development anti-patterns are recurring development activities that relate with defective IaC scripts. Goal: The goal of this paper is to help practitioners improve the quality of infrastructure as code (IaC) scripts by identifying development ac- tivities that relate with defective IaC scripts. Methodology: We identify devel- opment anti-patterns by adopting a mixed-methods approach, where we apply quantitative analysis with 2,138 open source IaC scripts and conduct a sur- vey with 51 practitioners. Findings: We observe five development activities to be related with defective IaC scripts from our quantitative analysis. We iden- tify five development anti-patterns namely, `boss is not around', `many cooks spoil', `minors are spoiler', `silos', and `unfocused contribution'. Conclusion: Our identified development anti-patterns suggest -
QUARTERLY CHECK-IN Technology (Services) TECH GOAL QUADRANT
QUARTERLY CHECK-IN Technology (Services) TECH GOAL QUADRANT C Features that we build to improve our technology A Foundation level goals offering B Features we build for others D Modernization, renewal and tech debt goals The goals in each team pack are annotated using this scheme illustrate the broad trends in our priorities Agenda ● CTO Team ● Research and Data ● Design Research ● Performance ● Release Engineering ● Security ● Technical Operations Photos (left to right) Technology (Services) CTO July 2017 quarterly check-in All content is © Wikimedia Foundation & available under CC BY-SA 4.0, unless noted otherwise. CTO Team ● Victoria Coleman - Chief Technology Officer ● Joel Aufrecht - Program Manager (Technology) ● Lani Goto - Project Assistant ● Megan Neisler - Senior Project Coordinator ● Sarah Rodlund - Senior Project Coordinator ● Kevin Smith - Program Manager (Engineering) Photos (left to right) CHECK IN TEAM/DEPT PROGRAM WIKIMEDIA FOUNDATION July 2017 CTO 4.5 [LINK] ANNUAL PLAN GOAL: expand and strengthen our technical communities What is your objective / Who are you working with? What impact / deliverables are you expecting? workflow? Program 4: Technical LAST QUARTER community building (none) Outcome 5: Organize Wikimedia Developer Summit NEXT QUARTER Objective 1: Developer Technical Collaboration Decide on event location, dates, theme, deadlines, etc. Summit web page and publicize the information published four months before the event (B) STATUS: OBJECTIVE IN PROGRESS Technology (Services) Research and Data July, 2017 quarterly -
VES Home Welcome to the VNF Event Stream (VES) Project Home
VES Home Welcome to the VNF Event Stream (VES) Project Home This project was approved May 31, 2016 based upon the VNF Event Stream project proposal. In the meantime the project evolved and VES is not only used by VNF but also by PNF (physical network functions). However, the term "VES" is established and will be kept. Next to OPNFV and ONAP also O-RAN, O-RAN-SC and 3GPP are using VES. The term "xNF" refers to the combination of virtual network functions and physical network functions. Project description: Objective: This project will develop OPNFV platform support for VNF and PNF event streams, in a common model and format intended for use by Service Providers (SPs), e.g. in managing xNF health and lifecycle. The project’s goal is to enable a significant reduction in the effort to develop and integrate xNF telemetry-related data into automated xNF management systems, by promoting convergence toward a common event stream format and collection system. The VES doc source, code, and tests are available at: OPNFV github (generally updated with 30 minutes of merged commits) OPNFV gitweb To clone from the OPNFV repo, see the instructions at the Gerrit project page Powerpoint intro to the project: OPNVF VES.pptx. A demo of the project (vHello_VES Demo) was first presented at OpenStack Barcelona (2016), and updated for the OPNFV Summit 2017 (VES ONAP demo - see below for more info). The following diagram illustrates the concept and scope for the VES project, which includes: From ONAP a Common Event Data Model for the “VNF Event Stream”, with report "domains" covering e.g. -
Metrics for Gerrit Code Reviews
SPLST'15 Metrics for Gerrit code reviews Samuel Lehtonen and Timo Poranen University of Tampere, School of Information Sciences, Tampere, Finland [email protected],[email protected] Abstract. Code reviews are a widely accepted best practice in mod- ern software development. To enable easier and more agile code reviews, tools like Gerrit have been developed. Gerrit provides a framework for conducting reviews online, with no need for meetings or mailing lists. However, even with the help of tools like Gerrit, following and monitoring the review process becomes increasingly hard, when tens or even hun- dreds of code changes are uploaded daily. To make monitoring the review process easier, we propose a set of metrics to be used with Gerrit code review. The focus is on providing an insight to velocity and quality of code reviews, by measuring different review activities based on data, au- tomatically extracted from Gerrit. When automated, the measurements enable easy monitoring of code reviews, which help in establishing new best practices and improved review process. Keywords: Code quality; Code reviews; Gerrit; Metrics; 1 Introduction Code reviews are a widely used quality assurance practice in software engineer- ing, where developers read and assess each other's code before it is integrated into the codebase or deployed into production. Main motivations for reviews are to detect software defects and to improve code quality while sharing knowledge among developers. Reviews were originally introduced by Fagan [4] already in 1970's. The original, formal type of code inspections are still used in many com- panies, but has been often replaced with more modern types of reviews, where the review is not tied to place or time. -
Whodo: Automating Reviewer Suggestions at Scale
1 WhoDo: Automating reviewer suggestions at scale 59 2 60 3 61 4 Sumit Asthana B.Ashok Chetan Bansal 62 5 Microsoft Research India Microsoft Research India Microsoft Research India 63 6 [email protected] [email protected] [email protected] 64 7 65 8 Ranjita Bhagwan Christian Bird Rahul Kumar 66 9 Microsoft Research India Microsoft Research Redmond Microsoft Research India 67 10 [email protected] [email protected] [email protected] 68 11 69 12 Chandra Maddila Sonu Mehta 70 13 Microsoft Research India Microsoft Research India 71 14 [email protected] [email protected] 72 15 73 16 ABSTRACT ACM Reference Format: 74 17 Today’s software development is distributed and involves contin- Sumit Asthana, B.Ashok, Chetan Bansal, Ranjita Bhagwan, Christian Bird, 75 18 uous changes for new features and yet, their development cycle Rahul Kumar, Chandra Maddila, and Sonu Mehta. 2019. WhoDo: Automat- 76 ing reviewer suggestions at scale. In Proceedings of The 27th ACM Joint 19 has to be fast and agile. An important component of enabling this 77 20 European Software Engineering Conference and Symposium on the Founda- 78 agility is selecting the right reviewers for every code-change - the tions of Software Engineering (ESEC/FSE 2019). ACM, New York, NY, USA, 21 79 smallest unit of the development cycle. Modern tool-based code 9 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn 22 review is proven to be an effective way to achieve appropriate code 80 23 review of software changes. However, the selection of reviewers 81 24 in these code review systems is at best manual. -
Fashion Terminology Today Describe Your Heritage Collections with an Eye on the Future
Fashion Terminology Today Describe your heritage collections with an eye on the future Ykje Wildenborg MoMu – Fashion Museum of the Province of Antwerp, Belgium Europeana Fashion, Modemuze Abstract: This article was written for ‘non-techy people’, or people with a basic knowledge of information technology, interested in preparing their fashion heritage metadata for publication online. Publishing fashion heritage on the web brings about the undisputed need for a shared vocabulary, especially when merged. This is not only a question of multilingualism. Between collections and even within collections different words have been used to describe, for example, the same types of objects, materials or techniques. In professional language: the data often is “unclean”. Linked Data is the name of a development in information technology that could prove useful for fashion collecting institutions. It means that the descriptions of collections, in a computer readable format, have a structure that is extremely easy for the device to read. As alien as it may sound, Linked Data practices are already used by the data departments of larger museums, companies and governmental institutions around the world. It eliminates the need for translation or actual changing of the content of databases. It only concerns ‘labeling’ of terms in databases with an identifier. With this in mind, MoMu, the fashion museum of Antwerp, Belgium, is carrying out a termi- nology project in Flanders and the Netherlands, in order to motivate institutions to accomplish the task of labeling their terms. This article concludes with some of the experiences of this adventure, but firstly elucidates the context of the situation. -
Software Reviews
Software Reviews Software Engineering II WS 2020/21 Enterprise Platform and Integration Concepts Image by Chris Isherwood from flickr: https://www.flickr.com/photos/isherwoodchris/6807654905/ (CC BY-SA 2.0) Review Meetings a software product is [examined by] project personnel, “ managers, users, customers, user representatives, or other interested parties for comment or approval —IEEE1028 ” Principles ■ Generate comments on software ■ Several sets of eyes check ■ Emphasis on people over tools Code Reviews — Software Engineering II 2 Software Reviews Motivation ■ Improve code ■ Discuss alternative solutions ■ Transfer knowledge ■ Find defects Code Reviews — Software Engineering II Image by Glen Lipka: http://commadot.com/wtf-per-minute/ 3 Involved Roles Manager ■ Assessment is an important task for manager ■ Possible lack of deep technical understanding ■ Assessment of product vs. assessment of person ■ Outsider in review process ■ Support with resources (time, staff, rooms, …) Developer ■ Should not justify but only explain their results ■ No boss should take part at review Code Reviews — Software Engineering II 4 Review Team Team lead ■ Responsible for quality of review & moderation ■ Technical, personal and administrative competence Reviewer ■ Study the material before first meeting ■ Don’t try to achieve personal targets! ■ Give positive and negative comments on review artifacts Recorder ■ Any reviewer, can rotate even in review meeting ■ Protocol as basis for final review document Code Reviews — Software Engineering II 5 Tasks of -
Full-Graph-Limited-Mvn-Deps.Pdf
org.jboss.cl.jboss-cl-2.0.9.GA org.jboss.cl.jboss-cl-parent-2.2.1.GA org.jboss.cl.jboss-classloader-N/A org.jboss.cl.jboss-classloading-vfs-N/A org.jboss.cl.jboss-classloading-N/A org.primefaces.extensions.master-pom-1.0.0 org.sonatype.mercury.mercury-mp3-1.0-alpha-1 org.primefaces.themes.overcast-${primefaces.theme.version} org.primefaces.themes.dark-hive-${primefaces.theme.version}org.primefaces.themes.humanity-${primefaces.theme.version}org.primefaces.themes.le-frog-${primefaces.theme.version} org.primefaces.themes.south-street-${primefaces.theme.version}org.primefaces.themes.sunny-${primefaces.theme.version}org.primefaces.themes.hot-sneaks-${primefaces.theme.version}org.primefaces.themes.cupertino-${primefaces.theme.version} org.primefaces.themes.trontastic-${primefaces.theme.version}org.primefaces.themes.excite-bike-${primefaces.theme.version} org.apache.maven.mercury.mercury-external-N/A org.primefaces.themes.redmond-${primefaces.theme.version}org.primefaces.themes.afterwork-${primefaces.theme.version}org.primefaces.themes.glass-x-${primefaces.theme.version}org.primefaces.themes.home-${primefaces.theme.version} org.primefaces.themes.black-tie-${primefaces.theme.version}org.primefaces.themes.eggplant-${primefaces.theme.version} org.apache.maven.mercury.mercury-repo-remote-m2-N/Aorg.apache.maven.mercury.mercury-md-sat-N/A org.primefaces.themes.ui-lightness-${primefaces.theme.version}org.primefaces.themes.midnight-${primefaces.theme.version}org.primefaces.themes.mint-choc-${primefaces.theme.version}org.primefaces.themes.afternoon-${primefaces.theme.version}org.primefaces.themes.dot-luv-${primefaces.theme.version}org.primefaces.themes.smoothness-${primefaces.theme.version}org.primefaces.themes.swanky-purse-${primefaces.theme.version}