According to SnapLogic, 61% of participants stated that artificial intelligence increased their productivity and efficiency. Software developers have various ways to utilize the power of AI. Although AI cannot write code as of now. It can optimize how developers write and improve code avoiding expensive and time-consuming errors.
For example, 2,300 of the 9,130 patents that IBM inventors received in 2021 had an AI-related subject. Elon Musk, the founder of Tesla and a titan of the IT industry, contributed $10 million to support ongoing research at OpenAI. The non-profit research organization that invented the recent ground-breaking AI tool called ChatGPT. Considering his $1 billion co-pledge of 2015, this donation is like a drop in the ocean.
Technology moved to algorithm and model-based machine learning and increasingly focused reasoning, invention, and evolution during a transformation period that began with “knowledge engineering” and continued over several decades marked by periodic dormancy.
Now, AI has taken back the center stage in a way that was impossible before, and it shows no sign to give it up anytime soon.
Software Development Redefined by AI/ML Solutions
The way we develop software as a whole is changing as a result of artificial intelligence and machine learning, from enhancing the quality of the code to cutting down on the time needed for tedious chores like testing and debugging.
Here are some of the ways ML and AL are transforming each phase in software development:
1. Gathering Project Requirements
When done incorrectly, the process of determining what end users need from a software product is a primary reason why projects are delayed, incur more costs, or completely crash. There is a number of digital tools that can analyze the requirements, point out errors and bugs, and recommend modifications.
And such tools are fueled by natural language processing (NLP), a component of AI and ML. To speed up the requirements review process, developers can use these tools can find inconsistencies or ambiguities. According to reports, businesses utilizing such technologies were able to cut the time needed for requirements gathering by over 50%.
2. Coding, Reviewing, Finding Bugs, and Fixing Them
AI-powered code completion tools suggest completing lines of code to developers as they write, cutting the number of keystrokes needed in half. A list of useful code snippets that ranks by relevance is also generated by certain tools. Some of them operate in a similar way to Gmail’s Smart Compose, a machine learning-based feature that proposes words or phrases to users while they compose emails.
Code-review tools, on the other hand, employ AI to automatically find defects and suggest code improvements by decoding the code’s intention and spotting frequent errors and their variations. A bug detection program used by Facebook predicts flaws and offers fixes that are so far proving effective 80% of the time.
This is crucial: In the later phase of the software life cycle, repairing vulnerabilities becomes much more expensive. Because it might be difficult to reproduce flaws in a developer’s local environment and it can cost a fortune to have business-critical services go down. Ubisoft, a video gaming company, stated that machine learning is enabling it to find 70% of issues before testing.
3. Testing and Quality Assurance
It’s been a long since automated testing systems help execute various test cases created by the QA team. Now in addition to running tests, Artificial intelligence makes it possible to automatically generate test cases as well. This helps ensure that more and more architectures and functionalities are evaluated while also saving time.
For example, in order to assess one particular software project, a private equity firm deployed an AI-powered application to automatically generate over 50% of the test cases. Moreover, by using these technologies, it may be simpler to differentiate real errors from the noise and pinpoint their underlying causes.
4. Deployment and Launch
Developers sometimes cannot find software flaws until after it has been installed in the target environment. However, by analyzing information from earlier code releases, statistics, and application logs, AI-powered software helps developers predict deployment failures as early as possible.
In case of a failure, this can accelerate the root cause analysis and recovery. One eCommerce company was able to deliver apps more quickly and reduce the mean time to recovery (MTTR). After a failure in the production environment by 75%, thanks to machine learning-based automated deployment check and rollback.
5. AI and ML are Transforming the Role of Software Developers
In the upcoming decade, the role of a software engineer may seem very different. So how it is now as the field is constantly changing. But it’s crucial to realize and keep in mind that technology will not ever replace the demand for developers. The idea that artificial intelligence will start to be able to write code on its own is still years away.
It is more likely for software developers to complete a variety of tasks and have the necessary resources to work efficiently with the help of AI and ML. In order to free up engineers’ time to work on more difficult challenges, AI has the potential to take on repetitive, monotonous tasks. This will lead to a very high demand for developers with experience, instead of replacing them,
Additionally, AI in business will probably be able to spot bottlenecks in available development technologies and help organizations anticipate upcoming software trends and updates.
The Present and Future is AI/ML
Similar to other industries, AI is making its way into software development services in an effort to both mimic and support human labor.
Although computers can already write code, we do not perceive this as a revolution in the industry. AI coding proficiency is still insufficient to take the position of a human programmer. It turns out that the capacity to evaluate the subtle yet complex relationships between software development phases is essential, and AI is not there yet. We can, however, predict that AI will prioritize boosting productivity over the coming decades
For the time being, software engineers can become much more productive by taking advantage of machine learning to make meaningful improvements. Sci-Fi authors can discuss the possibility of machines taking control.
The future holds much promise, and we eagerly waiting for it!
