CodeScene

@codescene

CodeScene is a social code analysis tool to prioritize technical debt. Read more at

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Vrijeme pridruživanja: svibanj 2016.

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  1. Prikvačeni tweet
    25. ožu 2019.

    CodeScene 3.0 is out! It's a complete re-design to make the analysis data easily actionable, fit into your existing workflows such as CI/CD and reporting, and to adapt the analysis information to different stakeholders. Check out all the great news here:

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  2. 31. sij

    We recently added support for the excellent to CodeScene. Check out our showcase analysis of the cool here:

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  3. proslijedio/la je Tweet
    29. sij

    CodeScene is a powerful visualization tool for identifying social patterns in your code, detecting delivery risks and managing technical debt. Get a free student account to analyze your repos with the Student Developer Pack.

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  4. 27. sij

    How healthy is your codebase? Can we monitor the code health of whole microservice systems too? Find out in our article:

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  5. proslijedio/la je Tweet

    A new episode is up with ! Adam and Robby discuss strategies for prioritizing where to start tackling technical debt, and how to improve the onboarding experience for developers new to a codebase

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  6. 20. sij

    We’re pleased to announce that CodeScene is included in the GitHub Student Developer Pack. This means that CodeScene is available for free to every student developer. Read more here: , ,

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  7. 2. sij

    With , the interesting unit of code analysis is not the file level but the system level. CodeScene lets you monitor the code health of each service just like you would supervise their run-time characteristics:

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  8. proslijedio/la je Tweet
    29. pro 2019.
    Odgovor korisnicima

    That's correct: Code Maat cannot do it, but its evolution in supports these measures. CodeScene on-prem integrates with additional data sources like Jira. I include some screenshots below, and there are more on our product page:

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  9. proslijedio/la je Tweet
    29. pro 2019.

    Everyone in software should try this. Code Scene now supports views that previously have only been available if you paid someone like me to build them for you! Great to see progress in showing change-over-time in

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  10. 29. pro 2019.

    CodeScene was created to give stakeholders a new vision of their software projects with specific and actionable information that enables software organizations to increase their delivery efficiency. Read our story here:

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  11. proslijedio/la je Tweet
    19. pro 2019.

    if you're interested in this topic, I talked to a while ago about applying bioinformatics methods to software, and he pointed me to , a tool that tries to quantify issues like knowledge loss or code fragmentation

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  12. 20. pro 2019.

    Thanks for the feedback! CodeScene was indeed designed as a reaction and complement to traditional tools. We have a blog on how CodeScene differs here:

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  13. 19. pro 2019.

    How does CodeScene pull off its technical debt predictions? Black Magic? No, reality is slightly more mundane (but not much): lots of historic data from real-world codebases which makes it possible to apply algorithms and machine learning:

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  14. 18. pro 2019.

    Thanks! Yes, in addition to its technical metrics, CodeScene also measures Organizational Complexity and predicts delivery risks. We have one example here:

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  15. 18. pro 2019.

    Shaping the next generation of software quality visualisation tools requires something extra. That's why CodeScene's advisory board is made up of industry leaders who have had a major impact on our field. Read more about CodeScene and our approach here:

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  16. 6. pro 2019.

    Meet , the founder of CodeScene, to see why we need to go beyond code to truly understand large-scale software systems:

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  17. 3. pro 2019.

    What happens when you add more people to a software project? Find out how you can detect and prevent negative scaling effects in your organization:

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  18. 28. stu 2019.

    Do you maintain the same quality on your test automation code as in the application code? Find out how you can explore your test code via our interactive visualizations. This example is from and is available here:

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  19. proslijedio/la je Tweet
    20. stu 2019.
    Odgovor korisniku/ci

    I use the cycle time from branch creation until it's merged. In practice, it's tricky to implement due to all different branch/merge strategies. We spent lots of time on implementing that support in :

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  20. proslijedio/la je Tweet
    19. stu 2019.

    I have a blog post on how I use to measure the social side of code, including Conway's Law:

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  21. proslijedio/la je Tweet
    Odgovor korisnicima

    In terms of looking more at the social metrics - code ownership comes to mind - this is where ’s is worth a look, as this could likely help identify “anti-Conway” situations

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