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Artificial intelligence (AI) and machine learning (ML) algorithms have emerged as game-changers in the constantly changing world of software development. They are changing how software is conceptualized, created, and employed, bringing in a new era of efficiency and innovation.
As the need for software solutions develops, so does the pressure to create faster, more robust, and more user-centric applications. The ability of AI to automate routine tests, predict potential problems, and improve performance is generating greater responses from developers. Along with it, the ease of working with a distributed tech talent workforce is creating a global community of talented workers with fresh perspectives to accelerate software development.
But not everyone is happy with the introduction of AI into the software development field; there are many who feel it may take away jobs from real people. While it has been established time and again that this may be a myth, rumors and possibilities abound.
This blog is all about how AI and ML can change the face of software development. Let’s come back to discussing the myths or realities some other time!
Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative forces in the software industry in recent years, revolutionizing how software is developed, deployed, and maintained. These technologies indicate the beginning of a new age of innovation, efficiency, and user-centric experiences.
Statistics shows how about 87% of global organizations are of the strong opinion that AI gives them a much-needed competitive advantage. Another study claims that spending on AI and ML is going to increase by 37.3% from 2023 to 2030.
While we are still exploring more ways to incorporate it into our everyday use, it can make things simpler and more efficient in some software development. Even when there is an ongoing debate on how AI can take over the 'human' aspect of software development (and it may still be under the scanner for many years), one thing is sure: AI and ML can enhance and improve development efficiency in the following ways:
The use of AI and ML to generate code can help developers expedite operations and reduce errors and bugs in the process. AI can help in identifying code templates and patterns and generating and customizing codes according to the input. There are several AI-based coding tools, such as CoPilot, CodetT5, Tabnine, etc., that can code in not just one but several languages.
For instance, CodeX is another AI tool that generates codes in 12 languages and can also translate between the different languages. While there is an on-going debate about how AI can replace developers very soon, many field experts believe it can be a great learning experience for early coders. Such AI-based code generation can also help make the developer’s life easier since the initial brainstorming ideas from the AI prompts can help in its further development.
CodeMonk, for instance, has developers who produce quality products with much less turn-around time, thanks to the innovative AI algorithms.
The use of mathematical and statistical methods to predict a future outcome is called predictive analytics. With the help of AI and ML algorithms, we can predict the future trend of a software program or the value and statistics of a product to ensure its credibility in the market.
For example, when developing a new product, the use of predictive analytics can help identify loopholes or futuristic possibilities, such as how it will work, say, 5 years down the line, or so on. With such outcome-based software development, it can help reduce failures or redundancies in a program or product.
Bug fixing is an integral part of software development, sometimes more so than the actual developmental process. A survey show how about 38% of developers feel they spend a quarter of their developmental time on bug fixing rather than on product building. With bug detection and fixing taking over a major chunk of the work, sometimes about 70% of the production time, there is a need here to find a way to fasten up the process.
Check out how CodeMonk’s developers use AI’s bug detection for on-time delivery of products
It is also a well-known fact that bug-fixing during the initial design stage can reduce errors and bugs considerably. According to research, the cost of detecting a bug in the design stage is six times less than fixing it during the implementation stage.
With AI tools helping with bug fixing at an earlier stage, organizations can:
There are many AI-based debugging tools employed by organizations that have helped streamline software development. One such example is Facebook (Meta)’s SapFix, which is an AI-hybrid tool that helps in debugging and automatically fixing up the bugs for an error-free operation.
Have you heard of GitHub Copilot or Amazon’s CodeGuru? What they do is code refactoring—modifying a code without changing its exterior to make it more readable and efficient.
In other words, code refactoring is reorganizing existing code to make it easier to read and manage without changing how it works on the outside. Refactoring is a typical approach in software development that involves modifying code without changing its exterior behavior to improve quality, efficiency, and maintainability.
And, as in other things, AI algorithms make it happen for us by using techniques such as NLP, predictive refactoring, code smell detection, code pattern recognition, and automated suggestion. This not only speeds up the developmental process but also improves the efficiency and quality of the software developed.
NLP, or natural language processing, is another AI tool that comes with a treasure trove of usability in software development and testing. A report suggests that the market for the use of NLP is about to increase significantly from 24.10 billion US dollars in 2023 to 112.28 billion US dollars by 2030. With such popularity, many in software development are harnessing its powers to do their work.
To define NLP: Natural Language Processing (NLP) is a branch of AI that focuses on instructing computers to understand, interpret, and generate human language.
Natural Language Processing (NLP) can significantly improve productivity, accuracy, and communication across the software development and testing processes. NLP can help in various stages of development and testing, such as:
Many project managers have encountered the challenges of having numerous developers working on different sections of a project and discovering issues after merging their work with the main branch. That is where CI/CD comes into play.
Continuous Integration and Continuous Deployment (CI/CD) are software development strategies that strive to automate developing, testing and delivering code changes to production settings. While CI focuses on integrating code changes from multiple developers into a shared repository as often as possible, CD goes one step further by automating the deployment of code changes to production or testing environments.
So, what does CI or CD do? Simply put, CI assists in automating the process of integrating changes to code into a single database. This makes it easier to find bugs or other problems. With CI, the developers can immediately find issues and bugs and fix them for faster processing.
In Continuous Deployment (CD), automated code changes through pre-determined tests are released to the production environment. CD helps scale up operations and speeds up the time to market of a product—a dream scenario for any project manager. Customer value and satisfaction are additional bonuses in such scenarios.
There are several CI/CD servers that one can use, such as TeamCity, Bamboo, CircleCI, Jenkins, GitLab, etc.
The use case of AI and ML does not end here; it’s the beginning of the transformation phase, and further research will bring in several more possibilities to explore.
There is, however, one aspect that needs saying here:
AI and ML can help augment human capabilities and ease devOps processes, rather than replacing great minds!
These technologies can help automate repetitive tasks, optimize the testing process, enhance creativity, predict anomalies, and much more. There are limitless possibilities ahead, boundless innovation, and robust software solutions in the future. The question is, how flexible are we to embrace change and remain flexible to adapt to these changes?
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