Ai in Software Development

Introduction

As the software development landscape evolves, AI tools have become essential in enhancing efficiency and speed throughout various phases of the software development life cycle. At Devon, we adopt Agile Engineering Lifecycle (AEL) methodologies alongside a DevOps mindset. This article outlines how different AI tools assist in each of the five stages of software development, demonstrating their practical applications in real-world scenarios.

1. Planning Phase: Utilizing Confluence AI

In the planning phase, product management teams can leverage Confluence AI for content creation. By simply clicking on an AI icon while creating a page, product owners can generate strategic roadmaps based on provided prompts. For example, a user can request a roadmap for a new project over two months, dividing tasks on a weekly basis starting from a tentative date. This feature results in neatly organized tables that track dates, tasks, and status, significantly reducing the time spent in planning.

Moreover, the AI component offers an option to modify the tone of the document, allowing for a more tailored presentation style.

2. Project Management: JIRA AI

Moving into project management, JIRA AI plays a vital role in creating epics and stories. Users can generate comprehensive descriptions and acceptance criteria for each epic and story by simply inputting the main summary. For instance, when creating an epic related to UX research for a shopping cart, essential steps can be automatically suggested by the AI, saving time and ensuring consistency. Additionally, the AI can assist in generating detailed story descriptions based on basic user prompts.

3. Development Phase: GitHub Copilot

During the development phase, GitHub Copilot serves as a primary assistant. Developers can use it to generate database schemas and APIs effortlessly. For example, with a prompt like "create database tables for SQLite," the AI generates the necessary SQL code complete with table structures and insert statements. In contexts requiring API specifications, developers can obtain JSON data structures or even generate REST controllers and test code, streamlining the development workflow.

In coding practices involving Test-Driven Development (TDD), Copilot can help create test cases tied to any modifications made in the service classes, automatically ensuring that changes in the primary code are mirrored with appropriate tests.

4. Quality Engineering Phase: Automated Testing

As projects transition to the quality engineering phase, AI tools continue to shine. Code generation for automated tests can be facilitated using Copilot, predicting and completing test cases based on previously written code. For example, if a login test case exists, the AI can predict the need for a logout test case with all necessary steps included.

Furthermore, using AI to create test cases for API verification has transformed testing in web applications. For instance, importing APIs from a Swagger definition allows users to generate vast test suites automatically, confirming the expected responses and their formats.

5. Bug Tracking: JIRA AI for Issue Management

In the final phase, JIRA AI proves invaluable for bug tracking. Users can create bug reports effortlessly; summaries and detailed descriptions can be auto-generated based on user input. Additionally, natural language prompts enable easier issue searches, abstracting the technical complexities traditionally involved in these tasks.

Ultimately, the integration of AI tools across these various phases profoundly empowers developers and product teams, enhancing their ability to deliver high-quality software more efficiently and effectively.

Keywords

AI tools, software development, Confluence AI, JIRA AI, GitHub Copilot, project management, database schemas, automated testing, bug tracking, Agile Engineering Lifecycle.

FAQ

Q1: How does Confluence AI assist in the planning phase?
A1: Confluence AI helps product management teams generate content, such as roadmaps and task breakdowns, through simple prompts, allowing for efficient organization and tracking.

Q2: What role does Jira AI play in project management?
A2: Jira AI aids in creating epics and user stories by automatically generating descriptions and acceptance criteria based on user inputs, streamlining project tracking.

Q3: How can GitHub Copilot enhance the development process?
A3: GitHub Copilot assists developers by generating code snippets, database schemas, and REST API controllers based on plain English prompts, as well as aiding in Test-Driven Development.

Q4: What benefits does AI bring to automated testing?
A4: AI tools can predict and generate test cases based on pre-existing code, significantly reducing the effort required in quality assurance and ensuring coverage of various scenarios.

Q5: How does JIRA AI streamline bug tracking?
A5: JIRA AI enables users to create bug reports automatically with generated summaries and descriptions, simplifying the process of issue management and allowing for natural language searching.