How AI is Transforming the Way We Develop Software
Over the last decade, one of the most transformative pieces of technology to shake up a range of industries has most definitely been AI. Not only has AI been adopted in healthcare, but it has also been made available to consumers in a wide range of products such as AI smartphones. This being said, it should come as no surprise then that AI has also transformed software development.
Machine learning technologies such as AI give us the capabilities to accelerate our already existing technology by pushing the traditional software development lifecycle (SDLC). By doing so, it presents an entirely new paradigm for innovation of new technologies. Developing computer programs traditionally involve specifying what the developer wants the system to do and then engineering the features of the technology to do so.
Before the introduction of AI, computers were still quite powerful. Their downfall, however, was that teaching them rules in a rule-based way was too rigid, which meant that without AI, it would be difficult for them to identify particular media for specifics. Our smartphones now are a great example of this. This need is not possible from traditional software development which is why AI has changed the landscape in a colossal way.
AI techniques in the form of machine learning would prompt the software developer to curate and prepare domain-specific data. This is then fed into learning algorithms which are continuously improved and iteratively trained. By doing so, the developer is giving the device machine learning capabilities, a model, which can deduce essential features and patterns from data without the need for human encoding. This new way of working means models can highlight perspective details that even humans may not have thought of. With this in mind, it is evident that the most profound impact of AI on computer programming is how we as humans, have in the past, executed software development and its capabilities, which has now changed.
Software development in the traditional manner is not by all means at an end. Training machine learning models is just one step towards practising AI technology. It is thought that only a fraction of real-world machine learning is actually composed of codes. This means that important components of these systems will still need to be handled by regular software. These include aspects of the software such as interfaces and security, much like smartphones from Smartphone Chcker. However, technologies developed using traditional SDLC still have the potential to benefit from MAchine learning and here's how.
It can take months and years of planning to turn business requirements into technology products. Machine learning can help shorten the process by enabling domain experts who are less technical to use visual interfaces and natural language when developing technologies. This will then help cut the prototyping process in half and let the business move on to reach their goals quicker.
In most cases, vast amounts of time is spent by developers debugging and reading through documentation. By using smart programming assistance, the time they initially put into debugging would be reduced. This means that intelligent aid would be able to offer just-in-time support through recommendations, best practice and code examples for the developers to use to solve issues more quickly.
The nature of programming assistance means that they can learn from past experiences which can help identify common errors during the development process. This means in the future, and it may be possible for the software to change dynamically and respond to mistakes without the need for human intervention.
For team collaboration and long-term maintenance, clean code is critical. As large organisations upgrade their technologies, large scale refactoring is unavoidable, causing a multitude of issues within the organisation. Machine learning can be used to help analyse codes and automatically optimise it for performance and interpretability.
Considering future development, the question that still remains is if AI can create Ai then there will be no need for humans to be involved in the development process. We already see the massive growth in AutoML solutions. These technologies aim to automate pieces of the machine learning model training process. It helps to reduce the workload on data scientists and engineers by enabling domain experts to train production-quality models.
One of the biggest roles of AI in connection to the lifecycle of software development is in testing. AI in software testing refers to AI powered tools for software testing and testing AI based products.
Evident from the information above, engineering and software development has seen a significant transformation over the years thanks to the introduction of AI. The aim of AI and software intelligent tools make software development easier and in a way, more reliable. The use of AI has given us machine learning that has helped change the platform from enterprise to consumer level.
On an organisational level the AI helps with intelligent coding and even accelerating business through prototyping. On a consumer level, AI enables smartphones from smartphone checker users to take pictures of photographer quality without the need of heavy and expensive equipment.
Looking into the future, it will be interesting to see how the future of software development transforms and if we discover another way AI can help or if there will be another shiney new piece of tech that will come along and take the place of AI. For now, Ai is showing great promise and might just be the transformation software development has been waiting for all this time.