Detailed Requirements Elaboration #

Please see detailed requirements (product backlog items) for the MVP in Product Backlog.

Note

• Product Backlog items in Sprint Queue or Ready To Implement stages have detailed Acceptance Criteria, since they have been prepared by team lead and QA engineer
• Product Backlog items in Backlog Queue are formulated, but do not have detailed Acceptance Criteria and Quality Attributes yet, since they have lower priority than others

Prioritized backlog #

Development methodology adopted by our team requires three distinct backlogs:

• Requirements Engineering Backlog
  • Transforms insights and ideas of team members into actionable increments (enriched user stories or intermediate action stories) using project strategy ( Hypotheses Map), enriched user flow ( Process-Experience Map), implementation forms specification ( Story Implementation Map), and target Technical Architecture
• Product Backlog
  • Used for increments (small valuable additions to the product) flow, ensures full increment specification, automation of these specifications, and multi-stage deployment with intermediate validations reaching the end user
• Technical Backlog
  • Used for technical tasks flow, ensuring testing and review of every task before committing it to any stable branch

Project specific progress #

Product Management #

• New increments added, existing ones advanced and validated, see Product Backlog for details
• Target Technical Architecture advanced significantly compared to previous week, security concerns handling included
• Specified and coordinated DevOps advancements, see Technical Backlog for details
• Validated and approved increments transition to production version
• Integrated security requirements into target Technical Architecture

Backend #

Realtime static analysis and deep static analysis on save using built-in LSP server and various static analysis tools with rendering native to JupyterLab are implemented

• See implemented backend tasks (they have “Backend” tag) in Technical Backlog

ML Engineering #

Separation of localized deep feedback as code warnings and non-localized feedback as markdown text is implemented

• See implemented ML tasks (they have “ML” tag) in Technical Backlog

DevOps Engineering #

A three-stage deployment system has been implemented and wrapped conveniently:

• dev deployment for developers and intermediate testing
• beta deployment for validating the value and match with acceptance criteria
• uat deployment as a stable deployment for stakeholders Additionally, JupyterLab session credentials have been secured

• See implemented DevOps tasks (they have “DevOps” tag) in Technical Backlog

Security Engineering #

• Conjur Open Source integration research
• Keycloak integration research
• Target Technical Architecture updated with researched security tools

Weekly commitments #

Individual contribution of each participant #

Dmitriy Prokopyev #

• Added new increments, existing ones advanced and validated, see Product Backlog for details
• Advanced target Technical Architecture significantly compared to previous week, security concerns handling included
• Specified and coordinated DevOps advancements, see Technical Backlog for details
• Validated and approved increments transition to production version
• Integrated security requirements into target Technical Architecture
• Conducted Sprint Planning and other meetings required to orchestrate project development

Egor Torshin #

• Prepared and completed various backend tasks, see Technical Backlog, user “Egor Torshin”
• Participated in system design, see Technical Architecture
• Developed a static analysis microservice (syntactic/deep)
• Supervised LSP deployment

Aziz Vundirov #

• Completed various backend tasks, see Technical Backlog, user “Aziz Vundirov”
• Reconfigured, implemented, and deployed the LSP-service (+ deep analysis integration)
• Configured and deployed real-time validation in JupyterLab
• Rewrote the functionality of main LSP-server for supporting the real-time code-checking
• Connected the JupyterHub, LSP-server and Deep Static Analysis microservice

Nikita Tiurkov #

• Prepared and completed various ML tasks, see Technical Backlog, user “Nikita Tiurkov”
• Implemented warnings generation functionality, see commit
• Implemented logging for semantic feedback microservice, see commit

Oleg Shchendrigin #

• Prepared and completed various ML tasks, see Technical Backlog, user “Oleg Shchendrigin”
• Prepared test cases for generated warnings, approved implementation, see testing results document

Marsel Fayzullin #

• Completed various DevOps tasks, see Technical Backlog, user “Marsel Fayzullin”
• Added support for multiple working deployments at a time, see corresponding commit
• Moved credentials out of jupyterhub config, see [corresponding commit](Moved credentials out of jupyterhub config)

Dmitriy Yashin #

• Conjur Open Source integration research
• Keycloak integration research
• Target Technical Architecture updated with researched security tools

Plan for Next Week #

• Account for all the new insights by updating the Hypothesis Map
• Account for strategy adjustments by updating the Process-Experience Map
• Finalize Story Implementation Map
• Finalize target System Design
• FInalize Functional Requiremets and Non-Functional Requirements documents
• Migrate deep LLM-generated feedback and statis analysis feedback to corresponding buttons near each cell of the .ipynb notebook
• Implement LLM-powered context aware chat for user clarifications on feedback
• Integrate multiple sessions support for every other implemented feature
• Integrate customized environments support (pre-installed packages, included datasets and template files for cases)

Confirmation of the code’s operability #

We confirm that the code in the main branch:

•  In working condition.
•  Run via docker-compose (or another alternative described in the README.md).