Developers Management System MVP

[sergfeldman]

Problem

In general, it is difficult to correctly assess the efficiency of developers.
If the goal is not only to assess but constantly maintain the high efficiency of developers for a long time, then the difficulty even significantly increases.

Because of the difficulty, there are negative outcomes:

1. Organizations experience management overhead simultaneously with difficulties in managing developers.
2. Project deadlines are postponed, and the ability to predict milestones completion is limited.

Solution

Data-driven AI-enhanced system for the process optimization and effective management of development teams.

Development intelligence systems, Engineering Intelligence

• provide in-deth analytics and usable real-time reporting
• provide insights and recommendations on the processes optimization
• helps to increase team and individual developers productivity

For organizations:
The system tracks and optimizes development actions to increase the overall performance of the development on GitHub.

The system operates on a few levels:

• Developer (personal developer efficiency that Alex requested).
• GitHub repo (efficiency of the repo development).
• GitHub project (efficiency of the project development).
• Organization (efficiency of all organization projects.

For developers:
The assistant that optimizes personal activity on GitHub.
https://github.com/orgs/ubiquity/discussions/18

Personal developer efficiency

Personal developer efficiency is defined in real-time based on his GitHub activity.

On a basic level, a scoring model can be used.
A scoring model can be based on the DORA, along as use other approaches.

On an advanced level. an impact map model can be used.

Classic DORA

• Lead time for changes
  Lead time for changes (LTC) is the time between a commit and production. LTC indicates how agile a team is—it not only tells you how long it takes to implement changes but how responsive the team is to the ever-evolving demands and needs of users.

• Deployment frequency
  Deployment frequency (DF) is how often you ship changes; how consistent your software delivery is. This metric is beneficial when determining whether a team is meeting goals for continuous delivery.

• Mean time to recovery
  Mean time to recovery (MTTR) is the average amount of time it takes your team to restore service when there’s a disruption like an outage. This metric offers a look into the stability of your software, as well as the agility of your team in the face of a challenge.

• Change failure rate
  Change failure rate (CFR) is the percentage of releases that result in downtime, degraded service or rollbacks, which can tell you how effective a team is at implementing changes.

DevPool Developer efficiency MVP

This is an MVP for the discussion (not implementation as is).
The MVP logic:

• Each criterion has a weight to influence the overall evaluation.
• Each criterion is normalized (reduced to a value that reflects its difference from the norm for a given situation).
• Criteria comprehensively assess results in several areas of the developer's activity.
• Criteria show both the quality and pace/progress of a developer.
• A minimalistic set of criteria is presented in this version. The next versions can enhance logic and the number of criteria.

Normalization methods:

• Percent
• Comparison with the average

Developer's activity on GitHub:

• With GitHub issues - assign, comment, close, etc
• With code - open, commit, merge, revert, comment, etc
• With CI - perform a release, etc

MVP criteria:

• Change success rate
  The percentage of a developer's pull requests that were merged and then not rolled back out of the total number of pull requests for that developer.

• Code stability
  An average number of changes requested by other developers when reviewing this developer's pull requests.
  For normalization, it is compared with the average time of all developers in this GitHub project.

• Issues throughput
  An average number of issues completed by this developer during the period.
  For normalization, it is compared with the average time of all developers in this GitHub project.

• Issues lead time
  An average time between when a developer was assigned to an issue and when his pull request that solves this issue was merged.
  For normalization, it is compared with the average time of all developers in this GitHub project.

Example

<style type="text/css"></style>

	Weight	Value
Change success rate	0.25	0.9
Code stability	0.25	0.95
Issues throughput	0.25	1.1
Issues lead time	0.25	0.8
Total		0.9375

Structure of metrics

<style type="text/css"></style>

Stability	Repository stability	Code stability
Stability	Repository stability	CI stability
Stability	Release success rate	Release success rate
Productivity	PR productivity	Time to start PR review
Productivity	PR productivity	PR lead time
Productivity	Issues productivity	Change/PR success rate
Productivity	Issues productivity	Issues throughput
Productivity	Issues productivity	Issues on time

[sergfeldman]

Collect data for analytics ubiquity/ubiquibot#109

[sergfeldman]

Intelligent Engineering Management Platform

Top platform
https://jellyfish.co/platform/
https://jellyfish.co/platform/engineering-signals/
https://jellyfish.co/platform/team-health/
https://jellyfish.co/resources/
https://jllyf.sh/10KPIs-eBook
https://jellyfish.co/5-slides-for-technical-leaders-to-show-their-board-thank-you/
https://m.youtube.com/@JellyfishSoftware

2. https://m.youtube.com/watch?v=vfuavv0kbng
3. https://jellyfish.co/product-tour-view/
4. 10-KPIs-EBO-2021.pdf

Value stream management (broader vision)
https://www.leanix.net/en/products/value-stream-management

Platform with AI
https://www.allstacks.com/

5. https://app.storylane.io/share/1cieuzewxqhh

List of platforms
https://www.g2.com/categories/value-stream-management

Sales Intelligence Platform

https://people.ai/solutions/
https://people.ai/product/opportunity-management/activity-benchmark/
https://people.ai/product/account-management/engagement-dashboards/
https://people.ai/solutions/sales/
https://technologyadvice.com/products/people-ai-reviews/
https://www.gartner.com/reviews/market/revenue-intelligence-platforms/vendor/people-ai/product/people-ai
https://www.trustradius.com/products/people-ai/reviews

1. https://www.g2.com/products/people-ai/reviews

DORA approach

https://devops.com/how-dora-metrics-can-measure-and-improve-performance/
https://cloud.google.com/blog/products/devops-sre/dora-2022-accelerate-state-of-devops-report-now-out
https://cloud.google.com/blog/products/devops-sre/using-the-four-keys-to-measure-your-devops-performance
https://dev.to/promyze/22-tools-to-compute-dora-metrics-4fbo

Open Source development management

https://www.trustradius.com/buyer-blog/open-source-project-management
https://project-management.com/open-source-project-management/
https://opensource.com/article/18/2/agile-project-management-tools
https://geekflare.com/open-source-project-management-software/

[sergfeldman]

Development success prediction and smart triggers based on the GitHub activity

https://www.linuxfoundation.org/resources/open-source-guides/measuring-your-open-source-program-success?hsLang=en
https://opensource.com/article/18/5/metrics-project-success
https://www.researchgate.net/publication/315605703_Success_or_Failure_Identification_for_GitHub%27s_Open_Source_Projects
https://github.com/topics/success-prediction
https://www.semanticscholar.org/paper/Investigating-Open-Source-Project-Success%3A-A-Data-Raja-Tretter/c8dddc68308c8476b1eb4173f392bad251f8315e
https://traxidy.com/the-best-ways-for-project-managers-to-predict-project-success/
https://github.com/RamkishanPanthena/Startup-Success-Prediction
https://www.researchgate.net/publication/315605703_Success_or_Failure_Identification_for_GitHub%27s_Open_Source_Projects
https://neverworkintheory.org/2022/04/04/contributor-prediction-for-github.html
https://medium.com/swlh/identifying-the-100-highest-potential-european-open-source-projects-on-github-33d4e8624290
https://youtu.be/u9jU4xJ03ek

[sergfeldman]

SPACE metrics
https://www.propelo.ai/blog/2022/space-metrics-how-to-get-started
https://codeclimate.com/blog/the-space-framework/
https://www.acumen.io/blog/space-framework-in-a-recession
https://analyticsverse.com/space-metrics
https://jellyfish.co/blog/what-is-the-space-framework/
https://www.allstacks.com/use-cases/space

Examples of the existing solutions

The example of how development analytics is used for reports ie investment reports
https://bitergia.com/resources/#reports

Paid or closed sourced solutions
https://waydev.co/pricing/
Cool features include
https://waydev.co/features/pr-workflow-analytics/

[sergfeldman]

Intelligent Engineering Management Platform

A more efficient way to manage a distributed crypto development team

In comparison with Jellyfish and other startups in this area the unique advantages of Ubiquity could be

• Focus on the specific processes if crypto development and crypto projects needs
• Tokenization of interactions and incentives in tokens
• Broad use of AI

Innovation

The room for innovation is a AI system that is proactively recommends and optimizes activity (not only analytic)

Marketing positioning

AI to turn the Engineering Management into bounties.
Bounty for

• Companies - increased overall delivery
• Managers - data-driven management that brings clarity and refuces effort on management
• Developers - real-time feedback on their performance and areas for improvement

Better to get additional funding to build this product

[sergfeldman]

Additional info in the comments
ubiquity/ubiquibot#154