Artificial Intelligence (AI) is smart enough to execute tasks that usually require human intelligence by learning from experience, or massive amounts of past data. Machine learning algorithms can be trained to look out for certain errors and automatically fix bugs during software development to ease the burden on software developers and improve a software’s time-to-market. In 2020, AI will only continue to grow sharper and more intelligent as it takes over the software development market.
Here are 3 important ways in which AI is set to disrupt software development as we know it:
Rapid Development of MVPs
Developing technology products from business requirements typically involves months, if not years, of planning and deliberate execution using massive amounts of resources and large budgets. Getting the idea to a Minimum Viable Product (MVP) level often required funding and lengthy development cycles. With the help of machine learning, however, this cycle can be reduced to a few lines of code by enabling developers to build technology products using either visual interfaces or pre-defined natural language libraries. Drag-and-drop functionalities can also help technical domain experts create simple prototypes rapidly, with the click of a few buttons. AI will help reduce software development to a few keystrokes as computers are trained to automatically develop software on their own. This can be achieved by training machine learning models to replicate the code used in past successful projects. In this way, rapid MVP development can be seen to improve time-to-market as well.
Efficient Project Management
Software development projects are notorious for going over budget or exceeding set timelines. Coming up with reliable estimates of both budgets and expected timelines requires deep expertise gained from past projects, forecasts of possible delays, and past data on common pitfalls and delivery times. Machine learning, when trained with data from logs of past projects like common bugs, user stories, estimates vs. actuals, can accurately predict the resources and the amount of time required to complete a project. As the machine learning program tracks a team’s performance in real-time, it can forecast potential delays, identify inefficient resource allocation, and provide recommendations for which new features to build and how to address common pitfalls in the process. Therefore, AI can transform software development by efficiently handling all project management requirements and monitoring the right metrics to keep the project on track.
One crucial component of software development is quality testing. Machine learning can be used to effectively identify errors in the development phase from past experience. Deep learning algorithms can help with pattern detection so that developers can identify and classify different error types and bugs with greater ease. This could, in turn, also help with speeding up the debugging process. Another key challenge of software testing is developing a comprehensive list of most likely test cases and forecasting extreme situations that may have a significant impact on the software’s performance. Machine learning algorithms can take on this challenge by reviewing past logs and generating useful test cases to automatically run through the system without any human intervention. Since machine learning algorithms can learn from experience to flag common errors automatically, less human intervention is required in the testing phase as well.
Software development will be completely transformed by Artificial Intelligence in 2020 as its capabilities only grow stronger with every passing day. Not only will it reduce a software’s time-to-market and minimize the amount of resources required to develop it, but it will also simplify bug detection and decision-making. This is largely due to its ability to identify coding errors faster than a human could and provide accurate recommendations for all potential software development pitfalls and challenges given the knowledge it has gleaned from past projects and system logs.
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