AI and ML will forever change the software development process. They won’t only speed up the development that human-driven software coding applies to solve problems. Also, expect gradual improvements in the accuracy and the high level of advancements in the end product; even though, the process can be far simpler. According to a research paper published by the University of Gothenburg, AI and ML technologies are increasingly being componentized. Even non-experts can use and reuse these components in their mobile app development projects.
An AI/ML-based software development process is no longer about defining if-then-else cycles. It’s more about selecting the right data and training the neural network that can solve a given problem without any human intervention. Applying AI and Ml technologies to software development is bringing in a revolution in the way problems are solved and the tools used. It’s also completely redefining of what a programmer does.
Let us have a look at some of the ways AI and ML technologies are helping the software development process:
Quick MVP Development
Traditional programming goes on at its own speed when it comes to implementing software rapidly. There is nothing you can do. As a result, you miss the opportunity and enter the market as a latecomer.
When a business wants to quickly launch an MVP, it will still require several months of programming. The prototype may help it get more funding, but if the prototype is not ready at the right time, the business can miss valuable opportunities. ML can shorten the cycle to a few lines of code or just drag or drop.
As mentioned already, the technology may still take time to turn up as a fully grown technology for large projects, but some good examples for smaller things do already exist. One of them is to build a chatbot either by using predefined natural language libraries or by using an easy-to-use, no-code platform.
Project Management
Typically, when it comes to project management, an experience manager goes by its past exposure to similar situations regarding delivery times, delays and the general drawbacks. Taking account of these things help the project managers try to keep things aligned, in terms of time and money.
If the data related to this process is stored, it can be applied to train AI/ML systems helping in managing a project more productively and generating accurate estimations of several things.
Using deep learning technology may come out as the best choice as these are, in fact, pattern-detection jobs. But this will become possible when you have detailed past projects’ details logs covering bugs, estimations, and real values along with user stores, reviews, etc.
AI / ML provides a great way to estimate the delivery schedule and stick to obligations as drawn in the initial contract.
Automatic debugging
When going more in-depth, pattern detection can identify and classify types of errors. With a deep learning algorithm using cloud management with Spell, it’s easy to flag known errors. This will help in speeding up the debugging process. It can also exceed a programmer and also learn to fix every surfacing error. After sufficient training, the debugging system could automatically correct a variety of mistakes, quite similar to autocorrect working on smartphones.
Smart Assistants
AI can help novices learn about programming environments much faster in comparison to the trial-and-error approach. Although a typical programming environment does also come with some embedded help like auto-completion, etc, AI and ML can completely advance the software development process and turn out to be a smart assistants helping human-developers speed up the development process. An AI-based tool can also act as a trainer, providing recommendations, offering code examples, or preventing simple coding mistakes
Automate Code Generation
When it learns the necessary patterns, the AI and ML-based solution for software development can also be used to generate codes automatically. It will use and put together some pre-defined modules. It’s also possible that the AI software development tool replaces the needs of some junior developers in the future.
Automate Testing
Testing is one of the most crucial components throughout the software development process. The key challenge in software testing is to create a complete list of likely cases and extreme conditions that can critically impact a program’s performance. By looking at past logs, AI can be applied to automatically generate a list of test cases to run through the system. Also, it can predict the outcomes of testing without even performing the actual test. It can focus on those areas which could be problematic as well as saving time in a project already running late.
Formation of Strategy
The development of software requires a long discussion over including and excluding certain features. In the traditional approach, a debate is held over which features have to be included and which ones are to be removed or left for later. Here, AI can help in generating simulations and provide results on the hierarchy of the best features. These features can work as a base for the success of an end product. The system will use rates for similar products, and learn from the voice of the customers, and their reviews on social media.
Disadvantages
Though most of the AI-powered algorithms offer excellent predictions and automatization, they all have a definite disadvantage. The learning method of an algorithm is entirely unclear to external observers. The only way to play with algorithms is to feed them with new data and then look at the results.
Will the programmers’ era end?
The points mentioned above may make you think that developers will become obsolete in a couple of years and AI, ML, and DL-powered systems will take their place. Well, this is not going to happen in the foreseeable future as it’s just the start of AI systems becoming more reliable. There is a long way to go; still, human interventions will be required.
Final thought:
There is immense hype around using AI and ML for software development, but the technique is still in its infancy. This will take a couple of years to emerge as a fully-grown method to be used on a larger-scale software development project. But once it’s deployed for this purpose, and for sure it would be done, we will have the software developed in a matter of a few days or a week.
Author Bio. : Sofia Coppol is a digital marketing expert in Rapidsoft Technologies which is a leading IT consulting company providing full range it services including, IoT application development, ERP software development, AI App Development, and big data app development solutions. Sharing is caring!
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