Node Js Code Review Checklist
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
The Sad State of Web Development Random Thoughts on Web Development
The Sad State of Web Development Random thoughts on web development Going to shit 2015 is when web development went to shit. Web development used to be nice. You could fire up a text editor and start creating JS and CSS files. You can absolutely still do this. That has not changed. So yes, everything I’m about to say can be invalidated by saying that. The web (specifically the Javascript/Node community) has created some of the most complicated, convoluted, over engineered tools ever conceived. Node.js/NPM At times, I think where web development is at this point is some cruel joke played on us by Ryan Dahl. You see, to get into why web development is so terrible, you have to start at Node. By definition I was a magpie developer, so undoubtedly I used Node, just as everyone should. At universities they should make every developer write an app with Node.js, deploy it to production, then try to update the dependencies 3 months later. The only downside is we would have zero new developers coming out of computer science programs. You see the Node.js philosophy is to take the worst fucking language ever designed and put it on the server. Combine that with all the magpies that were using Ruby at the time, and you have the perfect fucking storm. Lets take everything that was great in Ruby and re write it in Javascript, I think was the official motto. Most of the smart magpies have moved on to Go at this point, but the people who have stayed in the Node community have undoubtedly created the most over engineered eco system that has ever appeared. -
16 Inspiring Women Engineers to Watch
Hackbright Academy Hackbright Academy is the leading software engineering school for women founded in San Francisco in 2012. The academy graduates more female engineers than UC Berkeley and Stanford each year. https://hackbrightacademy.com 16 Inspiring Women Engineers To Watch Women's engineering school Hackbright Academy is excited to share some updates from graduates of the software engineering fellowship. Check out what these 16 women are doing now at their companies - and what languages, frameworks, databases and other technologies these engineers use on the job! Software Engineer, Aclima Tiffany Williams is a software engineer at Aclima, where she builds software tools to ingest, process and manage city-scale environmental data sets enabled by Aclima’s sensor networks. Follow her on Twitter at @twilliamsphd. Technologies: Python, SQL, Cassandra, MariaDB, Docker, Kubernetes, Google Cloud Software Engineer, Eventbrite 1 / 16 Hackbright Academy Hackbright Academy is the leading software engineering school for women founded in San Francisco in 2012. The academy graduates more female engineers than UC Berkeley and Stanford each year. https://hackbrightacademy.com Maggie Shine works on backend and frontend application development to make buying a ticket on Eventbrite a great experience. In 2014, she helped build a WiFi-enabled basal body temperature fertility tracking device at a hardware hackathon. Follow her on Twitter at @magksh. Technologies: Python, Django, Celery, MySQL, Redis, Backbone, Marionette, React, Sass User Experience Engineer, GoDaddy 2 / 16 Hackbright Academy Hackbright Academy is the leading software engineering school for women founded in San Francisco in 2012. The academy graduates more female engineers than UC Berkeley and Stanford each year. -
CROP: Linking Code Reviews to Source Code Changes
CROP: Linking Code Reviews to Source Code Changes Matheus Paixao Jens Krinke University College London University College London London, United Kingdom London, United Kingdom [email protected] [email protected] Donggyun Han Mark Harman University College London Facebook and University College London London, United Kingdom London, United Kingdom [email protected] [email protected] ABSTRACT both industrial and open source software development communities. Code review has been widely adopted by both industrial and open For example, large organisations such as Google and Facebook use source software development communities. Research in code re- code review systems on a daily basis [5, 9]. view is highly dependant on real-world data, and although existing In addition to its increasing popularity among practitioners, researchers have attempted to provide code review datasets, there code review has also drawn the attention of software engineering is still no dataset that links code reviews with complete versions of researchers. There have been empirical studies on the effect of code the system’s code base mainly because reviewed versions are not review on many aspects of software engineering, including software kept in the system’s version control repository. Thus, we present quality [11, 12], review automation [2], and automated reviewer CROP, the Code Review Open Platform, the first curated code recommendation [20]. Recently, other research areas in software review repository that links review data with isolated complete engineering have leveraged the data generated during code review versions (snapshots) of the source code at the time of review. CROP to expand previously limited datasets and to perform empirical currently provides data for 8 software systems, 48,975 reviews and studies. -
Crawling Code Review Data from Phabricator
Friedrich-Alexander-Universit¨atErlangen-N¨urnberg Technische Fakult¨at,Department Informatik DUMITRU COTET MASTER THESIS CRAWLING CODE REVIEW DATA FROM PHABRICATOR Submitted on 4 June 2019 Supervisors: Michael Dorner, M. Sc. Prof. Dr. Dirk Riehle, M.B.A. Professur f¨urOpen-Source-Software Department Informatik, Technische Fakult¨at Friedrich-Alexander-Universit¨atErlangen-N¨urnberg Versicherung Ich versichere, dass ich die Arbeit ohne fremde Hilfe und ohne Benutzung anderer als der angegebenen Quellen angefertigt habe und dass die Arbeit in gleicher oder ¨ahnlicherForm noch keiner anderen Pr¨ufungsbeh¨ordevorgelegen hat und von dieser als Teil einer Pr¨ufungsleistung angenommen wurde. Alle Ausf¨uhrungen,die w¨ortlich oder sinngem¨aߨubernommenwurden, sind als solche gekennzeichnet. Nuremberg, 4 June 2019 License This work is licensed under the Creative Commons Attribution 4.0 International license (CC BY 4.0), see https://creativecommons.org/licenses/by/4.0/ Nuremberg, 4 June 2019 i Abstract Modern code review is typically supported by software tools. Researchers use data tracked by these tools to study code review practices. A popular tool in open-source and closed-source projects is Phabricator. However, there is no tool to crawl all the available code review data from Phabricator hosts. In this thesis, we develop a Python crawler named Phabry, for crawling code review data from Phabricator instances using its REST API. The tool produces minimal server and client load, reproducible crawling runs, and stores complete and genuine review data. The new tool is used to crawl the Phabricator instances of the open source projects FreeBSD, KDE and LLVM. The resulting data sets can be used by researchers. -
Write Your Own Rules and Enforce Them Continuously
Ultimate Architecture Enforcement Write Your Own Rules and Enforce Them Continuously SATURN May 2017 Paulo Merson Brazilian Federal Court of Accounts Agenda Architecture conformance Custom checks lab Sonarqube Custom checks at TCU Lessons learned 2 Exercise 0 – setup Open www.dontpad.com/saturn17 Follow the steps for “Exercise 0” Pre-requisites for all exercises: • JDK 1.7+ • Java IDE of your choice • maven 3 Consequences of lack of conformance Lower maintainability, mainly because of undesired dependencies • Code becomes brittle, hard to understand and change Possible negative effect • on reliability, portability, performance, interoperability, security, and other qualities • caused by deviation from design decisions that addressed these quality requirements 4 Factors that influence architecture conformance How effective the architecture documentation is Turnover among developers Haste to fix bugs or implement features Size of the system Distributed teams (outsourcing, offshoring) Accountability for violating design constraints 5 How to avoid code and architecture disparity? 1) Communicate the architecture to developers • Create multiple views • Structural diagrams + behavior diagrams • Capture rationale Not the focus of this tutorial 6 How to avoid code and architecture disparity? 2) Automate architecture conformance analysis • Often done with static analysis tools 7 Built-in checks and custom checks Static analysis tools come with many built-in checks • They are useful to spot bugs and improve your overall code quality • But they’re -
Letter, If Not the Spirit, of One Or the Other Definition
Producing Open Source Software How to Run a Successful Free Software Project Karl Fogel Producing Open Source Software: How to Run a Successful Free Software Project by Karl Fogel Copyright © 2005-2021 Karl Fogel, under the CreativeCommons Attribution-ShareAlike (4.0) license. Version: 2.3214 Home site: https://producingoss.com/ Dedication This book is dedicated to two dear friends without whom it would not have been possible: Karen Under- hill and Jim Blandy. i Table of Contents Preface ............................................................................................................................. vi Why Write This Book? ............................................................................................... vi Who Should Read This Book? ..................................................................................... vi Sources ................................................................................................................... vii Acknowledgements ................................................................................................... viii For the first edition (2005) ................................................................................ viii For the second edition (2021) .............................................................................. ix Disclaimer .............................................................................................................. xiii 1. Introduction ................................................................................................................... -
Empirical Study of Vulnerability Scanning Tools for Javascript Work in Progress
Empirical Study of Vulnerability Scanning Tools for JavaScript Work In Progress Tiago Brito, Nuno Santos, José Fragoso INESC-ID Lisbon 2020 Tiago Brito, GSD Meeting - 30/07/2020 Purpose of this WIP presentation ● Current work is to be submitted this year ● Goal: gather feedback on work so far ● Focus on presenting the approach and preliminary results Tiago Brito, GSD Meeting - 30/07/2020 2 Motivation ● JavaScript is hugely popular for web development ○ For both client and server-side (NodeJS) development ● There are many critical vulnerabilities reported for software developed using NodeJS ○ Remote Code Executions (Staicu NDSS’18) ○ Denial of Service (Staicu Sec’18) ○ Small number of packages, big impact (Zimmermann Sec’19) ● Developers need tools to help them detect problems ○ They are pressured to focus on delivering features Tiago Brito, GSD Meeting - 30/07/2020 3 Problem Previous work focused on: ● Tools for vulnerability analysis in Java or PHP code (e.g. Alhuzali Sec’18) ● Studying very specific vulnerabilities in Server-side JavaScript ○ ReDos, Command Injections (Staicu NDSS’18 and Staicu Sec’18) ● Studying vulnerability reports on the NodeJS ecosystem (Zimmermann Sec’19) So, it is still unknown which, and how many, of these tools can effectively detect vulnerabilities in modern JavaScript. Tiago Brito, GSD Meeting - 30/07/2020 4 Goal Our goal is to assess the effectiveness of state-of-the-art vulnerability detection tools for JavaScript code by performing a comprehensive empirical study. Tiago Brito, GSD Meeting - 30/07/2020 5 Research Questions 1. [Tools] Which tools exist for JavaScript vulnerability detection? 2. [Approach] What’s the approach these tools use and their main challenges for detecting vulnerabilities? 3. -
Jetbrains Upsource Comparison Upsource Is a Powerful Tool for Teams Wish- Key Benefits Ing to Improve Their Code, Projects and Pro- Cesses
JetBrains Upsource Comparison Upsource is a powerful tool for teams wish- Key benefits ing to improve their code, projects and pro- cesses. It serves as a polyglot code review How Upsource Compares to Other Code Review Tools tool, a source of data-driven project ana- lytics, an intelligent repository browser and Accuracy of Comparison a team collaboration center. Upsource boasts in-depth knowledge of Java, PHP, JavaScript, Integration with JetBrains Tools Python, and Kotlin to increase the efcien- cy of code reviews. It continuously analyzes Sales Contacts the repository activity providing a valuable insight into potential design problems and project risks. On top of that Upsource makes team collaboration easy and enjoyable. Key benefits IDE-level code insight to help developers Automated workflow, to minimize manual tasks. Powerful search engine. understand and review code changes more efectively. Smart suggestion of suitable reviewers, revi- IDE plugins that allow developers to partici- sions, etc. based on historical data and intel- pate in code reviews right from their IDEs. Data-driven project analytics highlighting ligent progress tracking. potential design flaws such as hotspots, abandoned files and more. Unified access to all your Git, Mercurial, Secure, and scalable. Perforce or Subversion projects. To learn more about Upsource, please visit our website at jetbrains.com/upsource. How Upsource Compares to Other Code Review Tools JetBrains has extensively researched various As all the products mentioned in the docu- tools to come up with a useful comparison ment are being actively developed and their table. We tried to make it as comprehensive functionality changes on a regular basis, this and neutral as we possibly could. -
Phabricator 538D8f2... Overview
Institute of Computational Science Phabricator 538d8f2... overview Dmitry Mikushin (for the Bugs Course) . October 17, 2013 Dmitry Mikushin Test-drive at http://devel.kernelgen.org October 17, 2013 1 / 14 . What is Phabricator? The LAMP-based web-server + command-line client for: peer code review task management project communication And it’s open-source Dmitry Mikushin Test-drive at http://devel.kernelgen.org October 17, 2013 2 / 14 . Test drive! Browse to http://devel.kernelgen.org, login and look around Dmitry Mikushin Test-drive at http://devel.kernelgen.org October 17, 2013 3 / 14 . Key features Peer code review Integrated environment for request reviewing, tasks/bugs and versioning system - in web environment - in command line Ergonomic task interface Dmitry Mikushin Test-drive at http://devel.kernelgen.org October 17, 2013 4 / 14 . Key features Peer code review Integrated environment for request reviewing, tasks/bugs and versioning system - in web environment - in command line Ergonomic task interface Dmitry Mikushin Test-drive at http://devel.kernelgen.org October 17, 2013 5 / 14 . Peer code review: traditional 1 RFC: Developer publishes a patch with the source and tests 2 Other developers comment on the patch issues/improvements 3 After all issues are addressed, reviewers ACK patch for commit 4 Developer himself or someone with rw rights commits the patch Everything is over email Relationship with Bugs: fixes for PRs are also reviewed this way Dmitry Mikushin Test-drive at http://devel.kernelgen.org October 17, 2013 6 / 14 . Peer code review: Phabricator approach 1 Developer check-ins the patch to the Phabricator over the command line (passing lint/unit tests, if any) $ arc diff . -
Development and Deployment at Facebook
Development and Deployment at Facebook Dror G. Feitelson Eitan Frachtenberg Kent L. Beck Hebrew University Facebook Facebook Abstract More than one billion users log in to Facebook at least once a month to connect and share content with each other. Among other activities, these users upload over 2.5 billion content items every day. In this article we describe the development and deployment of the software that supports all this activity, focusing on the site’s primary codebase for the Web front-end. Information on Facebook’s architecture and other software components is available elsewhere. Keywords D.2.10.i Rapid prototyping; D.2.18 Software Engineering Process; D.2.19 Software Quality/SQA; D.2.2.c Distributed/Internet based software engineering tools and techniques; D.2.5.r Testing tools; D.2.7.e Evolving Internet applications. Facebook’s main development characteristics are speed and growth. The front-end is under continuous development by hundreds of software engineers. These engineers commit code to the version control system up to 500 times a day, recording changes in some 3,000 files. Naturally, codebase size unique developers by week commits per month 14 800 10 700 12 600 10 8 500 8 6 400 6 300 4 4 200 LoC [millions] 2 100 2 active developers 0 0 0 ’05 ’06 ’07 ’08 ’09 ’10 ’11 ’12 ’05 ’06 ’07 ’08 ’09 ’10 ’11 ’12 number of commits [1000s] ’05 ’06 ’07 ’08 ’09 ’10 ’11 ’12 Figure 1: Different aspects of Facebook growth: growth of the number of engineers working on the code, growth in the total activity of these engineers, and growth of the codebase itself. -
Zerorpc) by Jérôme Petazzoni from Dot- Cloud 73 10.1 Introduction
Marc’s PyCon 2012 Notes Documentation Release 1.0 Marc Abramowitz January 27, 2014 Contents 1 Stop Mocking, Start Testing by Augie Fackler and Nathaniel Manista from Google Code3 1.1 Modern Mocking.............................................4 1.2 Testing Today..............................................4 1.3 Injected dependencies..........................................5 1.4 Separate state from behavior.......................................5 1.5 Define interfaces between components.................................5 1.6 Decline to write a test when there’s no clear interface..........................5 1.7 Thank you................................................5 1.8 Questions.................................................6 2 Fast test, slow test by Gary Bernhardt from destroyallsoftware.com7 2.1 Goals of tests...............................................7 2.2 How To Fail...............................................9 2.3 Unit tests................................................. 10 2.4 The End................................................. 11 2.5 Questions................................................. 11 3 Speedily Practical Large-Scale Tests with Erik Rose from Votizen 13 3.1 Die, setUp(), die............................................. 13 3.2 Die, fixtures, die............................................. 13 4 Fake It Til You Make It: Unit Testing Patterns With Mocks And Fakes by Brian K. Jones 17 4.1 Your Speaker............................................... 17 4.2 What’s covered............................................. -
Sapfix: Automated End-To-End Repair at Scale
SapFix: Automated End-to-End Repair at Scale A. Marginean, J. Bader, S. Chandra, M. Harman, Y. Jia, K. Mao, A. Mols, A. Scott Facebook Inc. Abstract—We report our experience with SAPFIX: the first In order to deploy such a fully automated end-to-end detect- deployment of automated end-to-end fault fixing, from test case and-fix process we naturally needed to combine a number of design through to deployed repairs in production code1. We have different techniques. Nevertheless the SAPFIX core algorithm used SAPFIX at Facebook to repair 6 production systems, each consisting of tens of millions of lines of code, and which are is a simple one. Specifically, it combines straightforward collectively used by hundreds of millions of people worldwide. approaches to mutation testing [8], [9], search-based software testing [6], [10], [11], and fault localisation [12] as well as INTRODUCTION existing developer-designed test cases. We also needed to Automated program repair seeks to find small changes to deploy many practical engineering techniques and develop software systems that patch known bugs [1], [2]. One widely new engineering solutions in order to ensure scalability. studied approach uses software testing to guide the repair SAPFIX combines a mutation-based technique, augmented by process, as typified by the GenProg approach to search-based patterns inferred from previous human fixes, with a reversion-as- program repair [3]. last resort strategy for high-firing crashes (that would otherwise Recently, the automated test case design system, Sapienz block further testing, if not fixed or removed). This core fixing [4], has been deployed at scale [5], [6].