Artificial intelligence (AI) is impacting just about every business. As organizations incorporate augment intelligence and AI into their digital transformation, the way they approach projects is changing.
It’s no different in the world of software development.
To develop software with AI, usual processes should be different. In the past, software code was compiled using decision trees and logic paths. When problems occur, engineers track the logic flow to find the fault and then rewrite the code to debug it. When there are new things to add, the software might branch off into a new pathway.
AI is evolving developer roles
AI presents a challenge. Many tasks and decisions are too complex to run in a rules-based environment. It would be simply impossible to write code to address every possibility. This takes a decidedly different approach to development and it means a different toolset for developers.
AI-powered software is less about code writing. Pete Warden, software engineer and CTO of Jetpac (now owned by Google), goes so far as to say that “in ten years, I predict most software jobs won’t involve programming.” The job will be more about collecting, cleaning, labeling, and analyzing data that feed the neural networks that will train the algorithm.
Developers for AI and machine learning software typically utilize standard algorithms from open source libraries and proprietary sources to drive the decision trees. The magic is in training the resulting algorithms to assess and weigh data points.
AI and machine learning are impacting software development in a positive way, including shorter development cycles, cleaner codes, and better optimization.
Faster development cycles
Companies are shortening development times. Rapid prototyping is possible because there’s less coding. That means fewer technical domain experts as agile teams use natural language and visual interfaces to develop software.
Intelligent programming assistants can provide automated support and recommendations making any coding needed quicker. Assistants like Codota for Java or Kite for Python can handle code completion gleaned from millions of other programs.
Cleaner code and better optimization
Tasks like testing, bug detection, and fixes take up most of the developer’s time. By analyzing system logs and flagging errors, AI-driven software can find problems more quickly and suggest solutions. Consulting firm Forrester suggests developers may see their biggest gains using AI for automated testing and bug detection.
AI can detect patterns and automatically correct common errors. It can generate test case lists and automatically run them through systems to predict outcomes.
Code analysis can also lead to automatic optimization, especially when it comes to code refactoring. This can help with interpretability and performance.
In software development, there is a multitude of decisions to make. This encompasses which features to include (or not to include). AI can fuel these decisions by running simulations and modeling user behavior from existing software or testing. It can then generate a hierarchy of features that are most important to produce success.
Will AI replace software engineers?
Will AI eventually write software on its own and make traditional software engineers obsolete? Nobody is suggesting this will happen anytime soon, but what was once considered science fiction is becoming closer to reality. Deep learning tools like BAYOU are taking steps to do just that.
Developed by computer scientists from Rice University and funded by Google and the U.S. military, BAYOU examined 100 million lines of Java code which was fed through its neural network. It can be used as a search engine for coding. Enter a few keywords and BAYOU will create the code, including API idioms or snippets. It can be used by software engineers to create code modules and save development time.
A new generation of software developers
Developing software in an AI environment will need a new generation of custom software development companies. Beyond coding, they will need to have a deep understanding of data and algorithms.