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Bankim Chandra is Director & CEO of Dotsquares. Always committed to innovative solutions and mentoring the next generation in the industry.
We all know that artificial intelligence (AI) is pervasive in many areas of our lives. From the emergence of self-driving vehicles to voice-activated assistants such as Siri & Alexa, AI is reshaping how we lead our lives. But how is this revolution affecting the SDLC (software development life cycle) industry and the development of software, systems and applications on the market? Is AI a factor in their development, and how is it being used?
The Early Stages
Let’s go back a few years. Early AI-driven coding tools focused primarily on enhancing efficiency in repetitive tasks and reducing human error. This included basic testing and straightforward code generation, focusing on making the developers’ lives easier by reducing the loa of manual labor. Machine learning (ML) and natural language processing (NLP) brought about significant change. Now, AI algorithms can understand and produce human-level text: the basis of turning natural language queries into basic functional code. This reduced the workload on seasoned developers, allowing them to accelerate the more complex elements of the code structure.
Today, AI for software development permeates all aspects of the process, from the initial planning stages, where it predicts project timelines, to testing and, now, even autonomous deployment.
What’s Happening Today
Today, we have a plethora of tools available to developers. From GitHub Copilot & ChatGPT to Tabnine, there are plenty of choices. Each can make a big difference to the overall result in different ways. GitHub Copilot, for example, offers suggestions as code is being written, offering suggestions directly within the browser. The application can generate unit test cases, propose intelligent code fixes and answer some coding queries, including refactoring. Similarly, ChatGPT can be very effective in analyzing code, alerting the user to potential errors, making informative suggestions and assisting in identifying possible security vulnerabilities. All these elements will help a seasoned developer produce code quickly and effectively. And that is the whole purpose of these tools—to assist and not replace. The tools available now work best as enablers for the developer. Based on most current AI tools, the greatest benefit is derived from performing repetitive tasks. Those slow, laborious tasks that always must be completed can be left with a high degree of accuracy for an AI assistant to complete, whilst the developer can concentrate on the more technical or more challenging tasks as required.
One element that AI is making leaps and bounds in is cybersecurity. It’s pretty safe to say that machine learning is the most powerful technology in cybersecurity today. Through machine learning, we can train systems to learn from the data they are exposed to, allowing the computer to make predictions or decisions autonomously. Through this “cognitive” ability, AI is used both offensively and defensively across elements of cybersecurity. In an offensive capacity, AI can detect and react to differences in network traffic, indicating a potential unauthorized access point into a system. With defense, AI can reverse engineer exploits, allowing patches to be available before the exploit becomes public knowledge.
What’s To Come
So, what can the future of integrating AI tools in development hold? The trend of using tools to raise productivity is set to expand dramatically as the providers supply solutions for different programming languages, frameworks and environments. Final testing typically takes between 3 to 6 weeks—AI tools can dramatically cut that timeframe. Testing and debugging are going to be a clear focus for the future. Furthermore, AI is more pervasive in the project planning and management stages. Here, GitHub Copilot can easily integrate with market-leading project management software, embedded directly into project workflows, allowing it to suggest interceding in the project based on scope and timelines and new developments.
The consensus has been that the AI tools used in development will change the industry beyond recognition, seeing vast swathes of developers and teams with no work, in effect, replaced. If anything from the recent past, that will certainly not be the case in the short term. The tools are designed specifically to augment the development process. By allowing quicker, better lower-level code to be developed by AI, the developer can face more challenging tasks, dedicate more time to them and ultimately produce a better final product quicker.
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