6 Ways Machine Learning is Improving Software Development

Machine learning (ML) is a subset of artificial intelligence that uses algorithms to enable computers to learn different tasks without being explicitly programmed; the more training data a computer has access to, the more accurate it becomes. The technology enables computers to solve problems that aren’t easily solved using traditional methods and is commonly used for applications such as speech recognition, image recognition, natural language processing and autonomous driving. 

Of course, the current applications of machine learning are going to evolve as we move forward and that is going to be felt across every industry. Machine learning is beginning to change how traditional software development works at its core, which will have ripple effects to many applications. The technology enables systems to function independently and, as such, developers have more time to focus on innovation. As such, machine learning is making it possible for developers to create smarter, more efficient applications. Here are just six ways that machine learning is improving the world of software development:

1. Handling Code Reviews

Software has to meet a lot of functional and non-functional requirements and code needs to be clean and bug-free. Coding guidelines are there to ensure there are no unnecessary complexities, security concerns or performance issues. However, while following coding guidelines is necessary to ensure work standardisation, it’s no mean feat. To ensure that any development team follows coding standards, code review is needed, which can be incredibly expensive, not to mention time-consuming. 

Machine learning helps handle code reviews by providing tools that can automatically search for common deviations from coding standards. Using ML-powered tools to assist code reviews can reduce costs, improve efficiency and, most importantly, ensure there are no application security risks in the software that is being developed. Machine learning can help to review and restructure code, making it not only in line with coding guidelines but more readable, consistent and performant. The same ML-powered tools can be used for maintenance, helping to clean, debug and modify code quickly and shortening the entire process considerably. 

2. Delivering New Insights

Machine learning is going to become increasingly vital to all businesses to deliver insights and that is especially beneficial in complex environments such as software development and IT teams. When there are conflicting priorities, multiple solutions from different vendors, several frameworks and tools and many stakeholders, IT environments quickly become complicated. Not only does the environment need to be simplified, but insights are needed to help manage conflicting priorities. 

Machine learning tools can study code repositories and gain actionable insights around things such as legacy code, whether there is any code that isn’t maintained, how many apps aren’t adapted to the cloud, what is slowing down a development team and more. With these ML-based tools, teams can become more aligned, collaborate better, focus on reusing code, make processes more efficient and deliver better solutions as a result. 

3. Managing Project Forecasting

Software development projects can be incredibly complex, with schedules and costs to manage as well as quality and risk management. With the help of machine learning, project managers can forecast when projects will be completed with accuracy, with algorithms taking into account team composition, past performance and the speed at which a team is able to complete tasks. The result is that project completion dates aren’t based on best guesses; they’re based on science. 

With ML-powered tools, development companies can use external data and information repositories to identify risks, review project documentation and more. What’s more, with M-powered dashboards, all of the information is easily available, improving project accuracy and cost estimations and reducing time to market. 

4. Automating Testing

When it comes to software development, there are lots of manual, repetitive tasks which need to be managed. However, while rule-based automation is already used for some tasks, machine learning is creating the possibility to automate a whole new array of tasks. New ML-based code review tools can learn from source code repositories to find critical bugs, working with multiple programming languages and learning through thousands of high-quality open source repositories. The tools can analyse the intent of code and highlight critical security vulnerabilities. 

ML isn’t used for automation as much as you might expect at the moment but it’s expected to grow exponentially. Smart programming assistants can read technical documentation and debug code by sifting through massive volumes of data and self-correct anomalies. The technology can also help create tests themselves, which has the potential to vastly reduce development time. 

5. Improving Data Management

Machine learning is the perfect solution when it comes to big data as algorithms can learn from a wide range of patterns and trends. With this ability, response times are reduced considerably. Machine learning can integrate data from multiple sources in a much simpler way than traditional data indexing and without the huge memory consumption required. This means that developers can manage data pipelines much more effectively, 

6. Writing Code

Not only can ML be used to test, monitor and update code, by using the right combination of deep learning and code structure recognition, it can actually be used to write the code itself. While developers need to create the source code, ML can create subsets to accompany that code, fill gaps through self-learning and convert diagrams into code. And, in the process, ML models can assess risk, detect anomalies and ensure security processes are in place. With the use of machine learning, thousands of lines of code can be reduced into hundreds, saving a huge amount of time and resources and enabling developers to focus on more value-based work enhancing the code. 

Will Machine Learning Improve Your Next Project?

Machine learning is rapidly changing the field of software development, making it more secure, efficient and reliable than ever. Of course, building machine learning solutions and tools requires extremely niche skills and the technology isn’t something everyone has access to as yet. What’s more, while programmes can learn and improve, they don’t have the ability to understand emotions and truly mimic human brain activity in the form of an artificial neural network; you still need humans on your side who know what they’re doing. 

If you’re working with an experienced software development company, there is a good chance that they’ll already have incorporated machine learning tools into their development process, meaning that you can create more efficient, powerful and innovative applications for your business

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