Analyze and Visualize Git Log for Fun and Profit

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12 likes•8,236 views

Raimonds Simanovskis

Presentation at Ignite RailsConf 2012

Technology

Analyze and Visualize
Git Log
For Fun and Proﬁt
Raimonds Simanovskis

Code Metrics...
Lines of Cyclomatic
code Test complexity
coverage

rails_best_practices
saikuro
ﬂay reek
ﬂog roodi

Mining Rails — Learning
from your App’s Lifeline
Michael Feathers,
Corey Haines
RailsConf 2011

Multi-Dimensional
Analysis

Authors

Fil e
es i m
T

Multi-Dimensional
Analysis
Dimensions Measures
Authors Commits
File Extension Additions
Commit Deletions
Time Zone
Hour Fil e
Week Day es i m
T

9-to-5 disciplined
developer

Hours of day

Top all time
Rails code “deleters”

Mikel Lindsaar
“How to (not) kill Rails”

Changes per day of week
2004-2011

Friday by hours of day

What's hot

Numerous empirical software engineering studies rely on detailed information about bugs. While issue trackers often contain information about when bugs were fixed, details about when they were introduced to the system are often absent. As a remedy, researchers often rely on the SZZ algorithm as a heuristic approach to identify bug-introducing software changes. Unfortunately, as reported in a recent systematic literature review, few researchers have made their SZZ implementations publicly available. Consequently, there is a risk that research effort is wasted as new projects based on SZZ output need to initially reimplement the approach. Furthermore, there is a risk that newly developed (closed source) SZZ implementations have not been properly tested, thus conducting research based on their output might introduce threats to validity. We present SZZ Unleashed, an open implementation of the SZZ algorithm for git repositories. This paper describes our implementation along with a usage example for the Jenkins project, and conclude with an illustrative study on just-in-time bug prediction. We hope to continue evolving SZZ Unleashed on GitHub, and warmly invite the community to contribute.

SZZ Unleashed: An Open Implementation of the SZZ Algorithm

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Outfittery’s mission is to provide relevant fashion to men. In the past we relied purely on our stylists to put together the best outfits for our customers. Right now we are in the process of adding more and more intelligent algorithms to augment our human experts. This transition to become a data driven company has left its marks on our IT landscape: In the beginning we just did simple A/B tests. Then we wanted to use more complex logic so we added a generic data enrichment layer. Later we also provided easy configurability to steer processes. And this in turn enabled us to orchestrate our machine learning algorithms as self contained Docker containers within a Kubernetes cluster. It then really took us some time to realize that we actually had built a delivery platform to expose just any pure function that our mad data scientists come up with – directly into our microservice landscape. Now we just put their R&D experiments directly into production… The application that drives this platform was written 100% in Kotlin. Where coroutines, kotlin scripts and DSL’s were smashed together to fulfill our very specific needs. This talk will guide you through this journey, explain how this platform evolved along with evolution of Kotlin.

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Visual Recognition with Anki Cozmo and TensorFlow - Deployed on IBM Cloud viaKubernetes and Apache OpenWhisk https://github.com/nheidloff/visual-recognition-for-cozmo-with-tensorflow http://heidloff.net/article/visual-recognition-for-cozmo-with-tensorflow https://twitter.com/nheidloff https://github.com/anki/cozmo-python-sdk https://codelabs.developers.google.com/codelabs/tensorflow-for-poets https://www.ibm.com/cloud https://openwhisk.apache.org

Visual Recognition with Anki Cozmo and TensorFlow

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Slides of a session that I have given/will give at various developer conferences in H1 2018. Niklas Heidloff http://twitter.com/nheidloff http://heidloff.net Summary Article http://heidloff.net/article/when-to-use-serverless-kubernetes OpenWhisk https://openwhisk.apache.org https://github.com/ibm-functions/composer https://github.com/nheidloff/openwhisk-debug-nodejs Kubernetes https://kubernetes.io https://istio.io IBM Cloud http://ibm.biz/nheidloff Abstract There is a lot of debate whether to use Serverless or Kubernetes to build cloud-native apps. Both have their advantages and unique capabilities which developers should take into consideration when planning new projects. We will throw some light on the topics ease of use, maturity, types of scenarios, developer productivity and debugging, supported languages, DevOps and monitoring, performance, community and pricing. Cloud-native architectures shift the complexity from within an application to orchestrations of Microservices. Both Kubernetes and Serverless have their strengths which we will discuss. Besides the core development topics, developers should also understand operational aspects how complicated it is to maintain your own systems versus using managed platforms.

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True Git: The Great Migration

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These slides are part of a webcast we delivered, including the following parts: -- Introducing GitLab and some of its differences over Git -- Some interesting facts about GitLab -- Demonstrating full development life-cycle using GitLab -- Success story: we hosted host Matan Keidar from Thomson Reuters company, and he demonstrated how they use GitLab with Jenkins and Slack as part of their development life-cycle -- Questions and answers More information about GitLab is available at http://almtoolbox.com/gitlab

Slide: Introducing GitLab by ALMtoolbox

Noa Harel

Jkklapp py eml · git_hub

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What's hot (18)

SZZ Unleashed: An Open Implementation of the SZZ Algorithm

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Why I don’t want to develop iOS apps in Objective C

Helpful into to Rx

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When to use Serverless? When to use Kubernetes?

静的解析、はじまったな

PubSub++ - Atmosphere 2015

Charles_Qian_Resume

Alfresco Process Services (APS) and the Internet of Things

Product update Feb 20

Build, Deploy, and Scale Your First Web App Using DigitalOcean App Platform

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