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CodeScene: Get Deep Insights into Your (Clojure) Code [Clojure Vienna meetup - March 2018 ]

Juraj Martinka
Juraj Martinka

Come and meet CodeScene, a unique behavioral code analysis tool written in Clojure. This tool goes beyond traditional static analysis by looking for patterns in version control data to understand the history and evolution of a code base: unraveling things like hotspots, temporal coupling between modules and an interesting social view of the code. I'll briefly describe the ideas behind CodeScene, how it works and then show you an analysis of real Clojure code.

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CodeScene Get Deep Insights into Your (Clojure) Code 1
About (me) Java Clojure curiousprogrammer.net Empear - CodeScene Brno, CZ Malmo, SE 2
Agenda Motivation What is CodeScene Demo Under the hood Why (not) Clojure 3
Motivation Developers spend most of their time on doing modi鍖cations to existing code Technical Debt is inevitable and must be proactively eliminated. 4
5
Technical Debt 6
Motivation (2) Clojure bene鍖ts - dynamic, quick experiments => rapid development Technical Debt can accumulate quickly 7
Motivation (3) Not all technical debt is created equal - Return On Investment Static analysis ignores history Relying solely on humans expertise is ineffective and expensive We need concrete data to be able to deal with Technical debt effectively 8
What is CodeScene? Web-based code analysis tool that lets you prioritise technical debt, identify organizational inef鍖ciences, and detect code thats expensive to maintain. Language neutral On-prem & cloud version (codescene.io) 9
Demo: clojure.core 10
Demo: Technical Debt Hotspots - commit frequency or code churn Refactoring Targets - prioritised hotspots X-Ray Programming languages 11
Demo: Architecture Hotspots Conways Law 12
Demo: Social Analyses Individuals Owners Knowledge loss (Rich Hickey) Coordination needs - gvec.clj Authors 13
Demo: Project Management Branch statistics (re-frame) Delivery risk Lead Time To Merge 14
Demo: Early Warnings metabase 15
Under the Hood git log -> parse -> analyse -> store to disk -> present (UI) Pipes & Filters Core analysis module reused by cloud and on- prem applications Performance Libraries 16
Pipes & Filters Git -> git log -> parse -> analyze -> persist on disk -> load from disk -> present (UI) Pipes & Filters 17
Parsing 18
Analyses Language neutral - based on universal LOC metric X-Ray (ANTLR) Hotspots Temporal Coupling 19
Analyses (2) Core analysis module is completely separated and reused from both on-prem and cloud applications Results are stored in plain CSV 鍖les - no DB! 20
Performance Memory is the bottleneck => serialisation of analyses Linux ~45 min on i7 machine 21
Libraries ANTLR - microgrammars (X-Ray) Incanter - buggy ring & compojure selmer spec clojure.java.jdbc - H2 core.async - simple scheduler 3rd party integrations - clj-http, proxy-vole, clj-ldap, clj-slack etaoin - UI tests cprops - con鍖guration 22
Why Clojure? Its fun Productivity Quick feedback Fast innovations Data transformations 23
Thats it? Just these guys?! Carl Gustaf 24
Why Not Clojure(Script)? Server-side rendering with thin JS layer Jenkins plugin - Java Haskell - git cloning service Kotlin? (IDEA plugin) 25
Conclusion Technical debt is a real problem regardless of programming language Theres a huge amount of useful information stored in your version control system Ultimately, you need to rely on human expertise Support your developers judgment and experience with data to get the highest ROI 26
Resources CodeScene Introduction (15 min) screencast CodeScene Product Sheet CodeScene.io Showcase - clojure, erlang, React, etc. Adams talks & books Talk A Crystal Ball to Prioritize Technical Debt EuroClojure 2015 talk Beyond Code: Repository Mining with Clojure book Software Design X-Rays: Fix Technical Debt with Behavioral Code Analysis book Your Code as a Crime Scene CodeScene enterprise documentation Empears blog - e.g. The Day I Parsed a Monster code-maat repository 27
Go and Try! 28 https://codescene.io/

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CodeScene: Get Deep Insights into Your (Clojure) Code [Clojure Vienna meetup - March 2018 ]

  • 1. CodeScene Get Deep Insights into Your (Clojure) Code 1
  • 2. About (me) Java Clojure curiousprogrammer.net Empear - CodeScene Brno, CZ Malmo, SE 2
  • 3. Agenda Motivation What is CodeScene Demo Under the hood Why (not) Clojure 3
  • 4. Motivation Developers spend most of their time on doing modi鍖cations to existing code Technical Debt is inevitable and must be proactively eliminated. 4
  • 5. 5
  • 7. Motivation (2) Clojure bene鍖ts - dynamic, quick experiments => rapid development Technical Debt can accumulate quickly 7
  • 8. Motivation (3) Not all technical debt is created equal - Return On Investment Static analysis ignores history Relying solely on humans expertise is ineffective and expensive We need concrete data to be able to deal with Technical debt effectively 8
  • 9. What is CodeScene? Web-based code analysis tool that lets you prioritise technical debt, identify organizational inef鍖ciences, and detect code thats expensive to maintain. Language neutral On-prem & cloud version (codescene.io) 9
  • 11. Demo: Technical Debt Hotspots - commit frequency or code churn Refactoring Targets - prioritised hotspots X-Ray Programming languages 11
  • 12. Demo: Architecture Hotspots Conways Law 12
  • 13. Demo: Social Analyses Individuals Owners Knowledge loss (Rich Hickey) Coordination needs - gvec.clj Authors 13
  • 14. Demo: Project Management Branch statistics (re-frame) Delivery risk Lead Time To Merge 14
  • 15. Demo: Early Warnings metabase 15
  • 16. Under the Hood git log -> parse -> analyse -> store to disk -> present (UI) Pipes & Filters Core analysis module reused by cloud and on- prem applications Performance Libraries 16
  • 17. Pipes & Filters Git -> git log -> parse -> analyze -> persist on disk -> load from disk -> present (UI) Pipes & Filters 17
  • 19. Analyses Language neutral - based on universal LOC metric X-Ray (ANTLR) Hotspots Temporal Coupling 19
  • 20. Analyses (2) Core analysis module is completely separated and reused from both on-prem and cloud applications Results are stored in plain CSV 鍖les - no DB! 20
  • 21. Performance Memory is the bottleneck => serialisation of analyses Linux ~45 min on i7 machine 21
  • 22. Libraries ANTLR - microgrammars (X-Ray) Incanter - buggy ring & compojure selmer spec clojure.java.jdbc - H2 core.async - simple scheduler 3rd party integrations - clj-http, proxy-vole, clj-ldap, clj-slack etaoin - UI tests cprops - con鍖guration 22
  • 23. Why Clojure? Its fun Productivity Quick feedback Fast innovations Data transformations 23
  • 24. Thats it? Just these guys?! Carl Gustaf 24
  • 25. Why Not Clojure(Script)? Server-side rendering with thin JS layer Jenkins plugin - Java Haskell - git cloning service Kotlin? (IDEA plugin) 25
  • 26. Conclusion Technical debt is a real problem regardless of programming language Theres a huge amount of useful information stored in your version control system Ultimately, you need to rely on human expertise Support your developers judgment and experience with data to get the highest ROI 26
  • 27. Resources CodeScene Introduction (15 min) screencast CodeScene Product Sheet CodeScene.io Showcase - clojure, erlang, React, etc. Adams talks & books Talk A Crystal Ball to Prioritize Technical Debt EuroClojure 2015 talk Beyond Code: Repository Mining with Clojure book Software Design X-Rays: Fix Technical Debt with Behavioral Code Analysis book Your Code as a Crime Scene CodeScene enterprise documentation Empears blog - e.g. The Day I Parsed a Monster code-maat repository 27