By AI Trends Staff
Software developers are using AI to help write and review code, detect bugs, test software and optimize development projects. This assistance is helping companies to deploy new software more efficiently, and to allow a new generation of developers to learn to code more easily.
These are conclusions of a recent report on AI in software development published by Deloitte and summarized in a recent article in Forbes. Authors David Schatsky and Sourabh Bumb describe how a range of companies have launched dozens of AI-driven software development tools over the past 18 months. The market is growing with startups raising $704 million in the year ending September 2019.
The new tools can be used to help reduce keystrokes, detect bugs as software is being written and automate many of the tests needed to confirm the quality of software. This is important in an era of increasing reliance on open source code, which can come with bugs.
While some fear automation may take jobs away from coders, the Deloitte authors see it as unlikely.
“For the most part, these AI tools are helping and augmenting humans, not replacing them,” Schatsky stated. “These tools are helping to democratize coding and software development, allowing individuals not necessarily trained in coding to fill talent gaps and learn new skills. There is also AI-driven code review, providing quality assurance before you even run the code.”
A study from Forrester in 2018 found that 37 percent of companies involved in software development were using coding tools powered by AI. The percentage is likely to be higher now, with companies such as Tara, DeepCode, Kite, Functionize and Deep TabNine and many others providing automated coding services.
Success seems to be accelerating the trend. “Many companies that have implemented these AI tools have seen improved quality in the end products, in addition to reducing both cost and time,” stated Schatsky.
The Deloitte study said AI can help alleviate a chronic shortage of talented developers. Poor software quality cost US organizations an estimated $319 billion last year. The application of AI has the potential to mitigate these challenges.
Deloitte sees AI helping in many stages of software development, including: project requirements, coding review, bug detection and resolution, more through testing, deployment and project management.
IBM Engineer Learned AI Development Lessons from Watson Project
IBM Distinguished Engineer Bill Higgins, based in Raleigh, NC, who has spent 20 years in software development at the company, recently published an account on the impact of AI in software development in Medium.
Organizations need to “unlearn” the patterns for how they have developed software in the past. “If it’s difficult for an individual to adapt, it’s a million times harder for a company to adapt,” the author stated.
Higgins was the lead for IBM’s AI for developers mission within the Watson group. “It turned out my lack of personal experience with AI was an asset,” he stated. He had to go through his own learning journey and thus gained deeper understanding and empathy for developers needing to adapt.
To learn about AI in software development, Higgins said he studied how others have applied it (the problem space) and the cases in which using AI is superior to alternatives (the solution space). This was important to understanding what was possible and to avoid “magical thinking.”
The author said his journey was the most intense and difficult learning he had done since getting a computer science degree at Penn State. “It was so difficult to rewire my mind to think about software systems that improve from experience, vs. software systems that merely do the things you told them to do,” he stated.
IBM developed a conceptual model to help enterprises think about AI-based transformation called the AI Ladder. The ladder has four rungs: collect, organize, analyze and infuse. Most enterprises have lots of data, often organized in siloed IT work or from acquisitions. A given enterprise may have 20 databases and three data warehouses with redundant and inconsistent information about customers. The same is true for other data types such as orders, employees and product information. “IBM promoted the AI Ladder to conceptually climb out of this morass,” Higgins stated.
In the infusion stage, the company works to integrate trained machine learning models into production systems, and design feedback loops so the models can continue to improve from experience. An example of infused AI is the Netflix recommendation system, powered by sophisticated machine learning models.
IBM had determined that a combination of APIs, pre-built ML models and optional tooling to encapsulate, collect, organize and analyze rungs of the AI ladder for common ML domains such as natural language understanding, conversations with virtual agents, visual recognition, speech and enterprise search.
For example, Watson’s Natural Language Understanding became rich and complex. Machine learning is now good at understanding many aspects of language including concepts, relationships between concepts and emotional content. Now the NLU service and the R&D on machine learning-based natural language processing can be made available to developers via an elegant API and supporting SDKs.
“Thus developers can today begin leveraging certain types of AI in their applications, even if they lack any formal training in data science or machine learning,” Higgins stated.
It does not eliminate the AI learning curve, but it makes it a more gentle curve.