Artificial Intelligence (AI) is now a cornerstone of innovation, reshaping and altering the fabric of our daily lives. Our whole definition of interaction with machines and devices have been greatly modified with AI automating most repetitive processes. AI in software development helps with making significant strides in the building process. Right from the stage of requirements gathering and planning, to design, launch, and maintenance, AI helps with all the key stages of software development life cycle models.
Approximately 77% of devices currently in use incorporate some form of AI technology. A major chunk of the world’s devices utilizes AI in diverse functionalities, from basic features like spam filters to more advanced applications like voice assistants.
So, will AI replace software developers?
No, AI is unlikely to replace software developers entirely, but it can help them work more efficiently. Although AI can augment and empower the software industry, currently, it is not capable of filling vacancies for software engineers.
At Coding Sprint, a software development company we use AI as a handy tool to ensure faster-time-to-market ideas and build world-class solutions at cost-effective prices.
AI will impact many white-collar and clerical jobs, which make up a large portion of the global workforce. AI will likely impact professions most vulnerable to automation, such as writing, photography, and specific roles in software development.
Estimates of jobs likely to be replaced by AI
- By 2027, 23% of jobs will change due to AI (World Economic Forum).
- By 2030, AI could displace 300 million jobs globally(Goldman Sachs).
- By 2030, 14% of employees will have changed careers due to AI (McKinsey).
Why AI cannot replace software developers?
- AI is not yet able to think and adapt like a human. It can’t understand vague instructions and fails to analyze how code affects a business or coordinates with others.
- Current AI tools have limitations when it comes to coding.
- Foundational computer science knowledge and critical thinking skills are essential parameters that most AI development tools fail to exhibit with accuracy.
AI in Software Development Life Cycle (SDLC)
1. Planning and Requirements Gathering
The first and the most important phase of all SDLC models is planning and requirements gathering. This is the stage which shapes the success of the software product. Using AI in this phase helps with smooth market analysis & user research.
Machine Learning algorithms facilitate deep insights into large volumes of data. AI can analyze feedback from various stakeholders, including user surveys, reviews, and interviews, to extract key insights and prioritize features accordingly.
You can use AI to analyze business impact, user needs, and technical feasibility, allowing teams to focus on the most critical features first.
2. Design and Code Architecture
Wireframing user interfaces and prototyping tasks can be streamlined with AI tools like Figma, Canva, Mockup.ai, and others. These powerful design tools help to save time and are well-trained on machine learning algorithms. They can generate suitable designs based on the prompts and business goals.
3. Code Generation
AI software development coding tools like ChatGPT and DeepSeek can generate entire blocks of code from a natural language prompt. These tools are based on Machine Learning (ML) algorithms and are capable of generating highly accurate application code.
Depending on the coding challenge, ChatGPT can produce code with a success rate ranging between 0.66% to 89%. The average accuracy is around 66-69%.
DeepSeek-Coder-V2 consistently performs well on standard code generation benchmarks like HumanEval, achieving scores of 90.2%. This demonstrates high accuracy in generating functional code.
4. AI-driven Software Quality Assurance
AI based testing methods automates and enhances the software testing processes. AI engineers can use it for various tasks like generating test cases, identifying potential defects, and analyzing user behavior. The technology is also helpful with performing visual testing that allows for more comprehensive and efficient testing with less manual intervention.
By analyzing user interaction data, AI can prioritize test cases based on critical user flows and common user actions. You can manipulate AI to analyze historical performance data to identify bottlenecks and optimize resource allocation during performance testing.
Key AI testing statistics:
- Test coverage increase: AI-driven testing can increase test coverage by up to 85%.
- Cost reduction: Organizations using AI-based testing report a 30% reduction in testing costs.
- Efficiency boost: AI testing can lead to a 25% increase in testing efficiency.
- Market growth: The global AI-enabled testing market is expected to grow at a CAGR of 18.4% from 2023 to 2030.
- Future adoption: By 2025, 50% of new software projects are predicted to include AI-powered testing tools.
5. Deployment and Operations
AI driven deployment processes help to ensure optimal allocation of resources and build scalable software. AI based performance monitoring tools help to determine key problems and continuously analyse bottlenecks affecting the business growth.
There are many ways AI proves helpful in the launch and maintenance phase:
- AI can create targeted marketing campaigns based on user data. This increases the likelihood of engagement and conversion.
- Chatbots can provide 24/7 customer support, answering common queries and assisting users during the launch phase.
- AI can optimize CI/CD pipelines by identifying potential issues and streamlining the deployment process, leading to faster releases.
Conclusion
The accuracy of the AI model relies on high-quality data, so proper data collection and cleansing are crucial. Choosing the right AI model requires you to weigh specific needs of the software project.
While AI can automate many tasks, human expertise is still necessary for decision-making and interpretation of results. AI development company can help you build out-of-the-box solutions that drive growth and revenue.
