MavithaShehar/pickn-go-backend
Sep 2025 – Sep 2025
1 Month active
Languages Used
JavaScript
Technical Skills
Backend DevelopmentDatabase QueryingExpress.jsMongoDBMongooseNode.js
IT22269652
PROFILE
It22269652
During September 2025, Mavitha Shehar developed a vehicle search and filtering backend for the MavithaShehar/pickn-go-backend repository. She designed and implemented RESTful API endpoints using Node.js, Express.js, and MongoDB with Mongoose, enabling users to search vehicles by type, location, and price range. Her approach consolidated search logic into a single dynamic endpoint while also providing dedicated endpoints for flexibility and performance. The solution featured dynamic query construction and modular architecture, supporting optional parameters and scalable future enhancements. Although no major bugs were reported or fixed, her work improved vehicle discovery relevance and reduced search time for end users.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
236
Activity Months1
Your Network
20 people
Work History
September 2025
3 Commits • 1 Features
Sep 1, 2025September 2025 monthly wrap-up for MavithaShehar/pickn-go-backend. Key features delivered: Vehicle Search and Filtering backend with routes, controllers, and models; supports search by type, location, and price range with optional parameters and dynamic filter construction; consolidated into a single search endpoint and multiple dedicated endpoints for flexibility. Major bugs fixed: none reported this period. Overall impact: enhanced vehicle discovery, faster query responses, and a scalable foundation for future search enhancements, enabling improved customer satisfaction and conversion. Technologies/skills demonstrated: RESTful API design, modular backend architecture, dynamic query construction, incremental commits, and thoughtful endpoint consolidation.
3 Commits • 1 Features
Sep 1, 2025September 2025 monthly wrap-up for MavithaShehar/pickn-go-backend. Key features delivered: Vehicle Search and Filtering backend with routes, controllers, and models; supports search by type, location, and price range with optional parameters and dynamic filter construction; consolidated into a single search endpoint and multiple dedicated endpoints for flexibility. Major bugs fixed: none reported this period. Overall impact: enhanced vehicle discovery, faster query responses, and a scalable foundation for future search enhancements, enabling improved customer satisfaction and conversion. Technologies/skills demonstrated: RESTful API design, modular backend architecture, dynamic query construction, incremental commits, and thoughtful endpoint consolidation.
September 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaScript
Technical Skills
Backend DevelopmentDatabase QueryingExpress.jsMongoDBMongooseNode.jsREST APIs
Repositories Contributed To
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