How Does Machine Learning Change The Software Development Industry?
By Pete Campbell
Many new-age technologies are gaining primarily in popularity lately. You will frequently hear the buzz words as artificial intelligence, machine learning, deep learning, augmented reality, virtual reality, etc. Among these, machine learning models are now largely used to improvise the software application development lifecycle, thereby offering a new paradigm for inventing technology.
Application development now needs to meet very advanced specifications of the actual applications to be built and should have all such features hand coded. Even though the computers and processing technologies are so powerful, some tasks are still very complicated to be taught algorithmically.
Even those seemingly simple tasks, like identifying the objects on an image, cannot be performed fully successfully with the help of traditional application development approaches. Many IT engineers are still unlikely to list all the rules that will reliably recognize the objects in the image, which has several dependent variables. However, leveraging the benefits of machine learning will completely change the way as to how the software is built.
In standard development scenarios, machine learning will help eliminate the need to give computers instructions on how to make the decisions and take further actions. Developers prepare data which is then fed into the algorithms, and the system will further discern key patterns and insights from the data. More importantly, machine learning algorithms can also find similar patterns or details that the developers haven't thought of before.
Where to leverage ML capabilities?
Here are some areas in software development where you can leverage the capabilities of machine learning. It can help many organizations to do their work better and serve customers with the best experiences. They can offer customized solutions with ease.
Strategic decision making
Software development teams used to spend many hours discussing which features needed to be prioritized and which should be ignored. With machine learning, development teams can speed up this decision making by factoring in the success and failure of past developmental projects on these specific aspects and help the development teams and stakeholders to make data-driven decisions to minimize risks. For decision making database support, remote administration services of RemoteDBA.com can be explored.
Get precise development estimates
One major challenge developmental projects face is often going beyond the actual budget and also missing deadlines. To offer the most accurate estimates, dev teams need to have an insightful approach and profound experience of the given context. Machine learning will help analyze the data from various projects and also different aspects like the feature descriptions, forecasts, user stories and offer a more accurate estimate of the budget.
Besides all these, machine learning also doesn't need many experts on board to create such software applications. It will take many months to convert a given idea into an actual product. It needs to go through different development lifecycle steps, starting from brainstorming the idea to wire-framing the product prototype to finalizing it. While considering the actual development of the software, machine learning offers the potential to cut down the overall time spent on prototyping the products from a few weeks to many months as needed.
As discussed above, it is essential for software products to have a clean code, which is important in the long-term maintenance of the same and ensuring optimum team collaboration. With many companies evolving their technologies, the large-scale refactoring of codes is unavoidable many times. Here, we can use machine learning technologies which can be used to review the processes automatically, and code optimization can be done to ensure top performance.
Compilers can be programs that can process and translate the codes written in any high-level programming language to the machine language, which can be read and processed by computers. These can also automate the tasks to fix the given old code and expedite the next-gen codes. The compilers can also fix the old code without accessing the source code. This would also help develop applications in a few days, which need the effort of a seasoned developer for many months.
Adopting testing tools
As we can see, software testing is a very straightforward task. As far as we know how the systems behave, entering the inputs and comparing the outputs with the actual expectations is much easier. A proper match means that the testing is successful. It has to be tagged as a bug in identifying any mismatch, which has to be fixed by starting again from scratch. In such traditional testing and bug fixing cycle, the testers need to go through the given checklist or test case document to ensure that all the errors are reviewed and fixed. But the market is getting more competitive nowadays, and the customers are more and more demanding. In such a scenario, the traditional methods for testing software applications are not good enough to keep up with the increasing demands.
As we can see, developers tend to spend a lot of their time and effort going through the technical documentation and debugging the codes. By offering them guidance and support at the right time, like best practices, related text, examples of codes, smart programming assistants will significantly cut short the time for this process. Besides this, programming assistants can also be trained to learn from the previous experience to find errors and flat them automatically during the development cycle. Machine learning can more effectively be used to analyze the development system and to identify any potential errors. In the future, we can also expect that machine learning will let the software adjust itself in response to the errors without any human intervention.
To conclude, machine learning now has a very significant impact on the software development process. The development companies have to consider the impact of ML seriously to leverage the potential benefits it offers. It is not just how these applications are built but also to affect the nature and utility of the software itself positively. Machine learning can undoubtedly be a game-changer in the software development industry.