Artificial Intelligence and Machine Learning are the new hypes in the software industry. And almost every other industry out there. In fact, machine learning is changing the way softwares are created forever. It is making automation of more complex tasks possible. Tasks that were too complex to teach the machines in a rule-based way. Computers today are learning and performing advanced tasks without even being explicitly programmed thanks to machine learning.
In this article, we will dive into the details of how AI and machine learning are impacting software development. And we will start with what these terms actually mean.
What are artificial intelligence and machine learning?
Artificial intelligence is, simply put, the intelligence demonstrated by machines. It is a branch of computer science that deals with building smart machines capable of performing tasks that require human intelligence. It is not a replacement for human intelligence. Rather it learns and delivers human interactions.
Some very common examples of AI could be smart assistants like Siri or Alexa and self-driving cars. AI is actually more common in our day-to-day environment than we think. With the development of technology as we move many of our regular activities to digital spaces, AI is becoming an important part of our lives.
ML algorithms build a model based on training data in order to make decisions without being explicitly programmed. The term AI does get thrown around a lot. But machine learning is what’s really making automation possible. It is offering a new paradigm of inventing technology.
What’s the difference between AI and ML? This question was answered in The Book of Why by Judea Pearl. Machine learning learns and predicts based on passive observations. And artificial intelligence implies an agent to interact with the environment in order to learn and take actions towards achieving a certain goal. A software that predicts traffic is an example of a machine learning model. A virtual assistant is an example of AI.
Artificial intelligence and software development
What areas of software development are AI helping achieve greater efficiency in? Let us take a look!
In the traditional development processes, developers spend a significant amount of time managing coding documents. This often results in human errors such as spelling mistakes in code as well as wasting time that could be spent somewhere better. AI and ML can recognise historical coding patterns, helping softwares cut down on documentation time.
Moreover, AI assisted technology can improve real-time communication among different departments or even geographic locations by delivering accurate coding and testing history. AI algorithms can also search larger code databases looking for abnormalities, communicating possible future steps with developers to prevent them.
Designing a project requires a massive collaboration among developers, designers, R&D and marketing teams. This is a complex and demanding stage of development. AI can help streamline and automate the process by gathering data on what design approach to take. Artificial Intelligence Design Assistant (AIDA) is one such AI assisting in software design automation.
Testing is a major part of the software development cycle. Detecting bugs and errors is an important but time-consuming, costly task. Trained AI and ML algorithms can ensure the testing performed is error-free in less time. Which in turns enables code testers to focus on more important tasks such as maintaining code.
AI coding assistants such as Kite, Tabnine etc. can help with routine coding tasks to save time and increase efficiency for developers.
Software developers usually spend a lot of time making important decisions like which features to include in a product. AI can help with this by analyzing the performance and features of existing applications. This enables developers to prioritize features for future development much quicker and make data-driven business decisions.
Machine learning and software development
Let’s learn about the areas ML is playing a crucial role!
Mobile application services can find patterns in the big data collected from the users. This unstructured data can be used by machine learning algorithms to provide useful insight into user behaviour. Learning about what users are interested in and what are the sore points for them based on these insights help software developers enhance user experience to a large extent.
An example we are all familiar with is how YouTube uses its machine learning tools to give you a personalized experience. Or how a rideshare or food delivery service is using data from previous rides to estimate fare and arrival time taking traffic, weather and many other factors into account.
Software projects often go over budget or miss the deadline. Accurate estimations require a lot of analyzing and expertise from the development team. Machine learning can analyze data from past projects, such as user stories, features etc. to make a quite accurate prediction of budget and required time.
Be it for long-term maintenance or team collaboration – clean code is an essential most of the time. Machine learning can automatically review code written by a developer and optimize it for performance. Compilers can even fix old codes without requiring the source.