AI In the Software Development Lifecycle

AI Software Development Tools are another advancement in the developer's toolkit, but they are not a replacement for developers.

Artificial Intelligence (AI) has become part of modern software development. Tools that can generate code, assist with testing, or analyze project artifacts are already in widespread use. While these tools can be valuable, they also bring new risks and challenges. AI is here to stay in the software development world. Even if progress on these tools stopped today, the current generation of tools already allows developers to accomplish more work in less time than before. Despite this, developers still need to apply critical thinking, technical expertise, and domain knowledge to avoid making design or implementation decisions they later regret.

How AI Fits Into the SDLC

Depending on the organization and the development process, AI can appear at many points in the Software Development Lifecycle:

• Requirements Gathering & Analysis - summarizing documents, highlighting ambiguities, or suggesting missing requirements.

• Systems Design - generating draft diagrams or code skeletons, though designs must still be validated by developers.

• Development, Validation, and Testing - providing code completions, examples, or boilerplate. Generating test cases, identifying possible vulnerabilities, or flagging edge cases.

• Deployment - monitoring logs or detecting anomalies in system behaviour.

• Maintenance - suggesting bug fixes, dependency updates, or performance optimizations.

Benefits

The biggest advantage to using AI in development is productivity. When used carefully, it can help reduce time spent on repetitive or boilerplate tasks. AI suggestions can help break analysis paralysis, but the proposed path may not always be optimal.

AI also provides a form of support. While reading the documentation is still a very important skill that should not be replaced with AI, it can help with understanding high-level concepts or reframing things to make it easier to understand.

Risks

On the converse side, AI has some significant risks. These risks can be mitigated, but if you aren't active in managing them they will negatively impact your development. For example, the accuracy of AI-generated code is often poor. This is typically due to a lack of context, but also can be caused from the tool making incorrect assumptions such as whether a given method exists or not. AI outputs may also introduce security vulnerabilities or licensing issues if not carefully reviewed.

One of the biggest issues with over-using AI tools is an Overreliance on the tool which can negatively impact your way of thinking. Developers need to stay engaged in problem-solving themselves, and actively thinking and trying to understand things for yourself without an AI summary at the end is a very important skill!

In Summary

AI is a tool, not a replacement for developers. It can boost productivity, support learning, and help uncover issues, but it comes with real risks like inaccuracy, overreliance, and potential security or licensing problems.

Is there anything else I should add to this document to ensure students fully understand why not to rely on AI blindly?

Just kidding. The most important thing while using these tools is to apply your own judgement, be transparent with where and how you use the tools, and to stay actively engaged in problem-solving.