AI in Software Development

Introduction

Most articles published on AI futures either talk about Terminator-style apocalypse, or scoff at the same based on the difficulties of an AI developing sentience/consciousness/awareness. But is it necessary for an AI to be aware to become a problem? Maybe not, and maybe it's closer than we think.

Artificial intelligence (AI) is rapidly transforming many industries, including software development. AI-powered tools and technologies can today help developers automate tasks, improve code quality, and develop new and innovative products and services.

AI for Coding

AI-powered code generators can automatically generate code from natural language descriptions or other inputs. AI-powered code generators can be used for a variety of programming languages, including Python, Java, and C++. These code generators can be used to generate code for a wide range of tasks, such as writing functions, classes, and entire applications. This feature is particularly useful for novice programmers who may not know how to write specific code snippets. For example, GitHub’s Copilot is an AI-powered code completion tool that suggests whole lines or blocks of code as developers type.

AI-powered code reviewers can be used to automatically analyze code for potential errors and security vulnerabilities. These code reviewers can also be used to identify code that is inefficient, difficult to maintain, or simply contains deviations from coding standards. AI can identify syntax errors, runtime errors, and even some logical errors by analyzing the code and learning from past data.

AI can suggest improvements in the code structure without changing its external behavior, a process known as refactoring. It helps in improving the nonfunctional attributes of the software and can help developers improve their code to make it more efficient and maintainable.

AI for Testing

Software testing is a crucial part of the development process. It ensures that the software is working as expected and helps to identify any bugs or issues. AI can automate this process, making it faster and more efficient. AI algorithms can analyze the software’s behavior during testing and identify patterns that might indicate a problem. This allows developers to fix issues before they become major problems.

There are a number of ways AI can help test software:

• Test case generators can generate test cases from natural language descriptions or other inputs.
• AI-powered debugging tools can run a series of tests on the software and provide detailed reports on the results, including any bugs or issues it found.
• AI can help in performance testing by simulating user behavior and measuring how the software responds. It can identify bottlenecks and areas where performance can be improved.
• AI can simulate user interactions and measure how easy it is for users to complete tasks.
• AI can identify security vulnerabilities in the software by simulating attacks and measuring how well the software defends against them.

Automated Build and Deployment (CI/CD)

AI can automate the process of building software, which includes compiling code, running tests, and creating executable files (CI/CD pipelines).

There are a number of ways AI can help build and deploy software:

• AI can automatically build the code and test it, as discussed previously.
• AI can optimize the deployment process by analyzing past deployments and learning from them. It can suggest improvements in the deployment strategy, such as when to deploy, how to deploy, and where to deploy.
• AI can monitor the performance of deployed software, identify issues in real-time and suggest corrective actions.
• By automating the deployment process the risk of deployment errors is nearly eliminated.
• AI-powered monitoring tools can monitor the performance and stability of deployed applications and identify potential issues early.

AI-powered CI/CD tools are still under development, but they are already being used by some companies to improve their software development and delivery processes.

Gathering Requirements

AI is still in its early stages for requirements gathering, but it has the potential to revolutionize the process. NLP techniques can be used to understand and analyze requirements written in natural language., which helps identify ambiguities and inconsistencies in the requirements.

AI-powered tools can analyze customer feedback to identify common themes and patterns. AI-powered tools can also analyze market data, such as sales trends and competitor products, to identify emerging trends and opportunities in the market, as well as threats from competitors.

Once the requirements have been collected, AI-powered tools can be used to validate them to ensure they are complete, consistent, and unambiguous, followed by prioritizing them based on various factors such as business value, risk, and cost. AI-powered tools can then analyze past data to predict the impact of each requirement on the application’s success.

Conclusion and Next Steps

AI-powered software development platforms can help developers manage the entire software development process, from requirements to deployment. Developers who embrace these changes can expect to see significant improvements in efficiency and productivity.

AI can already help novice programmers with code creation and testing, but is not near the level of expert developers. The CI/CD process is more mature and in wide usage, although still typically created by and managed by humans. The requirements gathering phase is mostly still human, with some assistance from AI.

However, AI is still a relatively new technology, and it is evolving rapidly, so here are some predictions about its status within the next 5-10 years:

1. AI will be able to create code equal or better than expert developers. Possibly much better, to the level where not even experts can match it.
2. AI will be able to create and execute tests better than development or QA experts. Arguably the tests might not even be needed, given the code is being created by AI.
3. AI will create and flawlessly execute CI/CD processes.
4. AI will be able to collect requirements and translate those into directives to the coding engines with little to no assistance from humans.

At that point, the only element missing will be the linkage of all four of these steps under the guidance of one controlling AI. This is either achievable or close today, and certainly will be in place in 5-10 years.

Risks

The consequences of that last sentence must be carefully considered. Once we have AI controlling the entire process, from gathering requirements through deployment to production, we have created a self-evolving system that will make changes to software systems independently from humans. This means humans have no control, and likely little-to-no knowledge of how it operates.

This is not Skynet with consciousness and a hatred for humans. It will simply be tens of thousands of independent software systems with no intelligence or awareness, but with the ability to evolve as their requirements dictate. With this many instances in place (and this includes essentially every large software application or system), all operating with little to no human oversight, the probability of something going wrong seems very high.

Again, this isn’t some conscious entity like Skynet. It’s a banking application that decides everyone under 30 is a risk and therefore can’t get a loan. It’s a supermarket application that decides you have to pay cash rather than credit. It’s an electrical grid that decides to cut power to a hospital because it’s using too much. And so on…

Most probably the vast majority of these software applications will function perfectly, but if even 1% of them happens to go rogue, the result will be catastrophic.