Artificial intelligence and Machine learning have penetrated every sector. So, if you want to get to college, maybe an artificial intelligence robot will determine if you can get in. You see a car playing on the road which is driverless, after noticing it you know that there is the role of artificial intelligence in it. How can the software development be left behind?
Take, for instance, visual recognition of a person. If the person disguises himself and a snap is taken, can an ordinary computer recognize him as the same? A typical conventional software development team cannot possibly come up with an algorithm that will feed all the rules helping in recognizing a human in any disguise.
Enter AI and ML. Here, the rules and actions are not fed to the computer. But the curated domain-specific data is fed into algorithms to deduce features and patterns that are important. The point to be noted is that the output of ML and AI models can even surprise human intelligence.
What does a typical software development cycle contain?
The traditional software development cycle starts with the requirement definition or the technical specification.
Development wherein coders using a programming language to develop the codes.
Testing and Quality analysis where the built prototypes are tested to see if they are viable
Deployment here the passed product is sent to production and upgrades are released.
Maintenance The product has to be continuously maintained
Any further problem means that the requirement analysis begins and the cycle is repeated again.
The problem with the complex traditional software development is that it requires multiple levels of dependencies and integration and any mismanagement leads to inconsistencies and bugs.
Role of AI and ML in software development
Creating products based on requirements requires meticulous planning. The developers need to assess the needs and on that basis decide what features are to be included or excluded in the design. Here, the ML and AI can shorten the process by analyzing the past and present models. It helps the technical domain experts to make decisions faster and maximize the impact while cutting down the risks.
2. Error identification
Based on past programs, the programming assistants can identify common bugs and flag them during the development phase. Automatic code reviews can be conducted when it is pushed to the source code repositories. When an error is detected, it will be flagged with comments added. The necessary corrections will be done by ML, and it is sent back for coders to view and approve.
After deployment, the system logs can be analyzed to pick out errors and alert about it. This will automate many of the deployment and testing phases without the need for human intervention.
3. Code Restructuring
Refactoring is done to improve the internal code readability, complexity and maintainability without changing its external behavior. As organizations keep upgrading their technology, software refactoring is also needed to get a clean code that is critical for longterm maintenance.
With AI and ML, the existing codes can be analyzed and optimized for better performance. The codes will get more functional and use a natural language style.
In a typical deployment setup, the product is made ready for use. The upgrades are added and newer versions released. The continuous maintenance is undertaken so that bugs are fixed. The deployment stage is a high-risk stage, where if anything goes wrong, then the software code has to be tested, patched and probably worked upon again.
With ML and AI, the deployment task gets easier with automatic detection capabilities. In case of an error detected, that part is patched and tested after corrections using the ML. The product is then sent through the continuous delivery pipelines where efficient deployment and management takes place.
5. A complete software development cycle
From the initial design and planning to the last deployment and maintenance stage, AI and ML together will handle it under human observation.
The new age is where all the organizations will be trying to incorporate more of AI techniques so that they get better cost-efficient, intelligent solutions, and profitable results.
6. Simple programming
The amateur programmers will get a better understanding of the coding concepts as AI will help them to gain insights into good and neat coding. It will help them to collaborate on projects and fit in easily in an organization.
AI and ML will help them to learn from used test cases and share their queries with experienced coders so that they can advance their coding skills without any confusion of the code jargon.
7. Intelligent Code
The task becomes easy for coders as AI and ML will help them write more efficient codes and omit the redundant ones. The automated tool helps them identify the mistakes in coding. Also, with a better code editor, the coders can easily run it based on the programming in use.
Has AI the potential to wipe out coders and testers from their jobs?
Let us put this in another way. Though AI and machine learning are making things easier for software development, it is, after all, a machine program. It is not clever enough to understand the context and the people's complexity for the code like human beings.
So if coders have a flair for coding and are constantly improving their coding skills while delivering value to the business and society, then they need not fear. They can, in fact, be helped by AI and ML to leverage their task.
On the other hand, if coding is just your job and not your passion, then your job might well be at risk. Moreover, integration is that field in software development that still needs the skill of software engineers. So, definitely, there will be a shortage of coders and testers at least for this decade.