Generative AI Impact on software development
Software development is a complex, highly collaborative, and time-consuming process. Building a software product, application, or process requires a deep understanding of the domain, attention to detail, and deep testing. This year, Generative AI created a buzz in the tech and business communities and brought some fears to the minds of developers: “What happens to my job?”. I believe the human brain is complex in nature and not easy to replace. I see Generative AI not as a threat but as a genie to help developers in many ways to do their jobs well.
Automation is not new:
Automated program generators are not new in the world. Low code, no code platforms, and source code generation techniques have impacted the software industry tremendously over the last couple of years. However, Generative AI has penetrated multiple aspects of software development. Generative AI is changing the game forever. With tools like Chat GPT, GitHub Co-pilot, and Amazon CodeWhisperer, software developers can look at efficiency and quality.
In this article, we’ll explore how generative AI can positively impact productivity for software developers and the quality of the software, while also addressing the concerns and challenges that companies may face when considering the use of this technology.
Generative AI impact on software development:
With generative AI moving rapidly, many companies are analysing how and where to implement this technology. Before venturing into a full-fledged software development process, organizations must consider:
1. Use case: Organizations must consider the use cases for Generative AI. A clear roadmap for implementing AI for productivity and quality gains and the choice of Technologies. It is equally important to review any drawbacks of the technology.
2. Skillset: A tool that is not used effectively is equivalent to not using it. Developers need to understand the code that is being generated or the test cases that were automatically written. This means developers need to know not only how to connect the dots but also what each dot does. Generative AI and large language models are closely linked. Large language models, such as GPT-3 (Generative Pre-trained Transformer 3), are a type of generative AI that can generate new content, such as text, images, or music, based on a given input or context. The effectiveness of these models is largely based on their prompts and inputs. The team needs to go through training to get the desired results quickly and accurately.
3. Security and Data Privacy: Generative AI may require access to sensitive data, such as personal information, business logic, or confidential business data. Organizations should ensure that appropriate safeguards are in place to protect the privacy and security of this data.
How Generative AI Increases Productivity and quality?
With Generative AI, software developers can automate repetitive tasks, optimize code, and automate test cases, resulting in faster development and higher-quality software products.
1. Automating repetitive tasks: Generative AI can automate many repetitive and time-consuming tasks, such as generating boilerplate code, writing documentation, or testing software. This can free up developers’ time to focus on more complex and creative tasks, improving productivity and reducing the risk of human error.
Below is an example of boilerplate code for a basic Angular app.
2. Generating code: Some generative AI models can generate code based on natural language descriptions or examples, reducing the amount of time and effort required to write code from scratch. This can also improve code quality by reducing the risk of errors or inconsistencies.
Below is an example of code to create a RESTful API using FastAPI:
3. Improving code quality: Generative AI can be used to analyze code and identify potential errors or bugs. This can help developers identify and fix issues early in the development process, improving the quality of the final product.
Below is an example unit test for “is_anagram” function in Python:
4. Enhancing documentation: A generative AI tool can analyze a codebase or software application, identify key features and functionality, and automatically generate documentation that describes those features and how to use them. This can improve communication and coordination between team members, leading to better software development outcomes.
Below is an example of documentation of “is_anagram” function
5. Enabling new applications: Generative AI can enable new applications in software development, such as creating virtual assistants for developers and business teams, automating code refactoring, or generating user interfaces. This can lead to new opportunities for innovation and improvement in software development.
6. Vulnerability Detection: Generative AI automatically analyses code for potential vulnerabilities, such as SQL injection attacks, cross-site scripting, and Time complexity, based on input parameters. By identifying and addressing these vulnerabilities early in the development process, generative AI can help reduce the likelihood of security breaches.
Leveraging these tools effectively can bring a competitive advantage to software development teams and organizations.
PS: These are my personal views and opinions.
Stay updated with the latest news and updates in the creative AI space — follow the Generative AI publication.
