They say software is eating the world, but could artificial intelligence (AI) be eating the software world soon? Well, let’s first look at the explosive growth of artificial intelligence (AI) before we delve deeper into this question.
A Forrester survey shows that more and more development and delivery teams are confident that artificial intelligence (AI) in software development will improve development, agile test automation, and automation-testing software. It turns out that artificial intelligence (AI) tools can do a lot to make software development more reliable and make it faster and easier. It is being augured that artificial intelligence (AI) will change the way applications are developed. It will redefine the shape of software development. By 2021, AI tools are estimated to create $2.9 trillion in business value.
So when, in 2015, a Stanford Computer Science Ph.D. student used Recurrent Neural Networks to generate code, he was not playing a fluke. It looks like he took a Linux repository (all the source files and headers files), put them into one giant document, and then trained the RNN with this code. That was the first page of a revolution called Software 2.0. It is a world where developers will no longer need to write code. All they would need is relevant data that can be injected into algorithms and machine learning systems to write the required software.
So there will be developers who would curate, maintain, massage, clean, and label datasets. Then, those would take care of surrounding tools, analytics, visualizations, labeling interfaces, infrastructure, and the training code.
It is a world of automated code development where artificial intelligence (AI) and machine learning can automate the tedious parts of code development. It will liberate them from focusing on repetitive tasks.
Programming without artificial intelligence (AI)
Developers who do not have artificial intelligence (AI) by their side are doing well, but they can do better. They can use their time, creativity, and effort in doing complex, innovative, and design-side stuff. But right now, those who program without AI spend a lot of resources and hours on the following.
- Requirement gathering
- Tedious parts of planning and designing software projects
- Error-prone tasks like retracts and forward investigating plan
- Too much time spent to understand the needs as well as the desires of the client
- Code generation in a time- and labor-intensive way
- Writing code from scratch
- Deployment control
- Managing vulnerabilities because any failure in upgrades leads to risks in executing the software
- Bugs and error identification- Time spent in examining the executable files loaded with bugs and errors
Programming with artificial intelligence (AI)
But when artificial intelligence (AI) enters the scene, it changes the way software is written and thought of. Today we have tools like Google ML Kit and Infosys Nia, which automate specific processes to minimize human intervention to some extent. Artificial intelligence (AI) and machine learning (ML) can help in detecting loopholes early before moving to design. Natural Language Processing (NLP) can also make machines understand the user’s natural language requirements, leading to fast and smooth software models.
Artificial intelligence (AI) can make a lot of impact in some domains like medical sciences, robotics, process automation, and academic research. That’s why developers are using many libraries and alternatives to use artificial intelligence (AI) and machine learning (ML) in software creation and testing. Like Python, TensorFlow, SciKit-Learn, NLTK, among others. Look at how Cambridge University researchers can write working code after searching through a huge code database. It also shines the way towards an arrangement for the harvested code fragments and improving its efficiency. Then there is Diffblue, from the University of Oxford’s Computer Science department, which generates unit tests for code.
Microsoft’s Visual Code Intellicode helps the user with an alphabetical list of recommendations, and it skips the entire troublesome and time-consuming process that developers used earlier. There is C++ for multi-threaded programming. There is R for effective dynamically typed, scripting, procedural programming language. Prolog, a semantic inference engine for logic programming in AI/ML, is used for pattern matching over natural language parse trees. Here, AI programmers can feed in the data like facts and rules concerning the end goal. These tools can generate code in Java, Ruby, C#, Objective-C. There is LISP for quick AI prototyping, an exploratory macro system, compilers instead of interpreters, and an automatic memory manager, making it suitable for logic programming and machine learning. And there is Julia, that offers native or non-native libraries to make AI/ML development highly performant.
Ready for artificial intelligence (AI)
In the ‘State of AI for Enterprise Report’ by Teradata, respondents say AI has the potential to revolutionize their businesses. They imagine artificial intelligence (AI) and machine learning (ML) have the most significant potential to automate repetitive tasks, deliver new strategic insights, and automate areas of knowledge work. Many enterprises feel they will need to invest more in artificial intelligence (AI) technologies.
Well, artificial intelligence (AI) will make the software development part not just easy but more beautiful and exciting. It will empower developers to think big, better, and bolder. That’s why building relevant expertise to leverage artificial intelligence (AI) and machine learning (ML) in programming makes sense for programmers.