AI-Powered Productivity Tool

Problem Statement

In software development, developers provide estimated completion times for tasks, but there is no reliable way to verify whether these estimates are accurate. Common challenges include:

• Lack of visibility into whether a task's estimated time is well-constructed.

• Difficulty in analyzing actual development time versus initial estimates.

• No structured method for reviewing past deliveries to improve future estimates.

The Solution: CodiQ

Leanware designed CodiQ as an AI-driven tool that analyzes developers' work by examining GitHub commit history and the delivered code changes. As part of Leanware's expertise in AI-powered productivity tool development, CodiQ leverages AI to provide insights into task complexity and compares it with the actual time taken, helping teams improve their productivity and accuracy over time.

Development Process

1. Analyzing Developer’s work and estimates vs actual times.

CodiQ evaluates the developer’s work and task estimates provided and compares them with actual development time. The system identifies patterns in over- or underestimation, enabling teams to make data-driven improvements to their team’s productivity to increase their efficiency and quality.

2. AI-Powered Code Analysis

Using OpenAI's technology, CodiQ:

• Analyzes committed code to determine what changes were made.

• Summarizes code modifications and delivery timelines.

• Generates an AI-driven verdict on the

3. Connecting with GitHub

CodiQ seamlessly integrates with GitHub repositories to extract commit data, allowing it to:

• Track changes over time.

• Compare estimated and actual task durations.

• Provide teams with actionable insights.

4. Building the Web Application: Tech Stack

CodiQ was built as a cloud-based application with a modern tech stack:

• Frontend: React for a dynamic user interface.

• Backend: Python + Django for robust backend processing.

• Cloud Technologies: PubSub, Cloud Run, Cloud Run Functions for scalable operations.

• Database: PostgreSQL for structured data storage.

• AI Integration: OpenAI's API for automated code analysis and estimation validation.

5. Implemented User Journey

CodiQ was designed to provide a seamless experience:

1. Users connect their GitHub repository.

2. CodiQ analyzes commits and compares AI-generated estimates vs. actual task durations.

3. AI summarizes development patterns and provides a verdict on estimation accuracy.

4. Teams receive insights to refine future time estimations to enhance their productivity.

6. Testing and Optimization

Leanware conducted beta testing with development teams, gathering feedback to refine the tool. Adjustments were made to enhance:

• Accuracy of AI-driven summaries.

• Speed of data processing and commit analysis.

• User experience and reporting clarity.