Definitely, the introduction of ChatGPT back in 2022 raised interest across businesses, industries, and countries. It gave a push to the appearance of alternative tools and instruments like Gemini and Sora that are now trying to win market share. However, there are also concerns and questions related to how this new technology will influence the software development world. There’s no doubt it’ll push it further. But will we all gain or lose from it?
In this post, we’ll try to deliver the results of the research we conducted. If you’re related to the world of coding, this will answer your question and give you a better understanding of the future ahead of you.
Planning and Writing Requirements
This is one of the initial steps that any reliable software development company begins with. The flow encompasses defining stakeholders, product/project goals, and requirements analysis. The aim behind this step is for all participants (owners and coders) to get a clear understanding of software requirements.
Before AI: This is where misunderstandings usually start. With constantly growing user expectations, ongoing market changes, and dynamic business environments, project requirements are evolving, making it complicated for any team to keep up with changes. This often leads to uncontrolled project scope expansions, which, in turn, cause project delays and larger budget allocations.
With AI:
- NLP algorithms are effective for analyzing textual requirements, identifying vagueness, and providing suggestions and clarifications. This is of top importance for understanding project requirements.
- AI can automate the delivery of project documentation, helping to reduce manual effort and human error typical for any documentation-related assignments.
- AI uses historical data to predict scope changes and assist in managing a proactive scope, mitigating risks.
Setting up Software Architecture
Clearly, it’s critical to define the system architecture that will be scalable, secure, and efficient for the future development stages, API integrations, and more. If a team fails to do this, they will face inevitable complications at every development phase, reaching a dead end.
Before AI: The design and implementation of a scalable and secure architecture is time-consuming and requires vast experience. Considering all the evolving technologies and ensuring that the architecture remains adaptable is a huge challenge, especially when robust security measures should be kept in mind to avoid vulnerabilities.
With AI:
- Having analyzed specifications, business goals, and current architecture patterns, AI offers diverse architectural pattern recommendations to promote performance, scalability, and maintainability.
- AI can analyze all the current trends and help a software architect forecast emerging technologies.
- AI recommends design frameworks to be implemented to ensure high security.
- For cross-platform compatibility cases, Artificial Intelligence can evaluate diverse architectural design choices and offer options for optimized user experience.
Code Writing
Once requirements are written and the architecture of the future software is predefined, engineers start writing lines of code. With a predefined basis, it may seem simple. In reality, it’s not.
Before AI: Manual coding takes time. On the go, there are optimization challenges that are also time-consuming. For big projects, coordinating tasks and resolving issues in version control systems is tough. It’s complicated to conduct efficient code reviews to find errors.
With AI:
- Having learned from project requirements, generative AI automatically generated code snippets.
- It optimizes existing code to ensure better performance and compliance with existing industry standards.
- It delivers version control suggestions and assists developers in navigating through and resolving conflicts.
- ML and AI identify potential bugs and automate code review or suggest possible improvements.
Quality Assurance
QA engineers are the must-have part of any software development team. They evaluate how the software operates and ensure it meets both requirements and industry standards. At times, testing is not less complicated than coding.
Before AI: The flow of software testing includes diverse scenarios, most of which are performed manually, and thus they are typically error-prone. This causes issues for the correct analysis of system responsiveness and scalability. Additional challenges occur if testing is performed across devices and browsers.
With AI:
- AI tools analyze project requirements and the lines of code, compare them, and generate automated test scenarios to deliver a high-quality testing process.
- AI tools suggest and create performance test scenarios to streamline the work of quality assurance engineers.
- AI tools easily perform cross-platform and cross-browser testing to ensure the product works across environments.
Project Deployment
Once the project is developed, it’s time to release and install it, so that end-users can finally use it to satisfy their needs. In the real world, this process is as complicated as the previous ones.
Before AI: Deployment configurations differ depending on the environment. Eventually, this leads to deployment challenges, rollbacks, CI/CD pipeline complications, dependency management issues, and other potential conflicts.
With AI:
- AI-based technologies create special deployment scripts, considering the specifications of every environment
- AI tools are helpful in designing rollback strategies to help DevOps engineers minimize downtime.
- They optimize CI/CD pipelines and give hints on how to improve efficiency and/or reduce errors.
- They analyze dependencies, mitigate compatibility conflicts, and ensure error-free and smooth deployment.
Post-Production Maintenance
In post-production, a development team is expected to identify issues and resolve them. An advanced team can also prevent future problems, though they aren’t always successful in this.
Before AI: A human mind cannot always resolve issues before they impact end users. At times, the issues are detected once users are affected, and after they report problems to a support team. Why are errors detected after they occur? It’s because there is a poor scheme of knowledge transfer from a development team to a support team.
With AI:
- AI instruments can continuously analyze product performance, detecting potential errors and suggesting to the team how fixes are better to be implemented.
- AI-based software analyzes history and predicts possible future issues.
- AI updates an existing knowledge base, avoiding miscommunication between development and support teams.
Will AI Affect Software Development Roles?
It is already affecting development roles. For instance, the role of a Business Analyst is soon to be upgraded to drive the overall product strategy, where AI will write project requirements and user stories, while a BA will evaluate AI-generated ideas.
Designers will be using Artificial Intelligence to design UX/UI through graphical guidelines, user testing, and JavaScript design systems. Architects of software projects will use the power of AI to build custom platforms and governance systems, establishing new industry standards in code and development flow.
Quality Assurance engineers, in their turn, will use AI for continuous testing scenarios. The role of test architects is about to be introduced. Test architects will use AI to design, implement as well as maintain complex test architectures and perform testing for upcoming product features.
The area that remains untouched by AI yet is project management. Even with the whole array of tools for managing development processes and roles, PMs are here to stay for another couple of years, since they have the soft skills that AI cannot reproduce yet.
Conclusion
In the near future, Artificial Intelligence will totally change our traditional software development world. For better or for worse? As studies show, AI is here for improvements. Current development team roles won’t be erased, but they will be changed and enhanced. Dependence on AI-based technologies will allow development teams to offer more sophisticated approaches to planning and development. Eventually, the mix of development teams and AI will allow the clients of software agencies to grow and profit.
