Vitamin H

How Harnessing Artificial Intelligence Can Benefit Software Developers

By Chris Orozco


Could the rise of generative AI signal the beginning of the end for human software developers?

Artificial intelligence (AI) has become a hot topic of conversation as the technology finds its way to factories, healthcare facilities, data centers, and all points in between through driverless vehicles. The rise of generative AI, distinguished by an ability to create text or other content in response to user prompts, has been met by a combination of public fascination and fear as the potential to unseat traditional occupations becomes apparent.

Could the rise of generative AI signal the beginning of the end for human software developers? New and improved iterations of AI technology are released several times per month. With platforms like ChatGPT being called upon to assist with debugging and low-level coding tasks, it’s easy to imagine a not too distant future where human software development is eliminated.

Why then, does the U.S. Bureau of Labor Statistics predict a 25% uptick in software development opportunities over the next 10 years? Perhaps we humans underestimate our own value.

Will AI replace software developers?

White collar workers have been sounding the alarm over the threat of technological unemployment posed by AI, but software developers and software engineers appear to be especially concerned. In a recent survey of young software engineers, 40% indicated they were worried about the future of their profession. Already facing industry-wide layoffs, some tech professionals reported testing generative AI with coding tasks and being uncomfortably shocked by their proficiency. However, these developers may have some of their fears assuaged when they review the capabilities and limitations of AI.

The marvel of mundane task elimination

Natural language processing (NLP) is a sub-field of AI based on translating human readable text into meaningful code, which allows machines to understand and interpret human conversations. This capability is in full display when AI platforms suggest keystrokes for programmers, correct errors, or debug voluminous lines of code. These capabilities save valuable upfront development time while minimizing post-production bugs. Mundane coding tasks can also be automated using AI algorithms to further enhance efficiency.

AI as the developer’s ally

By minimizing human errors, making timely coding suggestions, and taking over repetitive programming and test processes, AI becomes the developer’s ally instead of their competition. In fact, developers using generative AI-based tools are more than twice as likely to report overall happiness. To continue this symbiotic honeymoon, it is important to understand and mitigate the inherent limitations of AI. Despite the obvious benefits, artificial intelligence cannot:

  • Display true creativity and problem solving abilities
  • Fully understand underlying principles and context
  • Keep up with trends and advancements in real time
  • Develop unique out-of-the-box strategies

Recognizing these weaknesses while exploiting the benefits casts the developer in the role of creator with AI acting as a valued and versatile tool.

The (new) role of the software developer

A successful partnership with artificial intelligence requires a re-imagining of the software developer role. The limitations of AI can lead to ethical issues that can only be mitigated by human oversight. Biased algorithms, incorrect assumptions, and a lack of context can inadvertently lead to errors or security issues that require human intervention to detect and mitigate.

Creativity is perhaps the most irreplaceable element of human software engineering. Generative AI is now capable of “creative” output mirroring that of the human brain, but these impressive results are ultimately constrained by the available training data. The original ideas and inspiration behind every significant software breakthrough are always the product of a human mind devoid of any such constraints.

AI use cases are expanding

Real-world examples provide evidence of optimized AI deployment, with new applications extending the symbiotic framework that serves as a model for the future:

  • Test case generation: AI-powered test case generators can suggest, create, and complete test cases seamlessly to eliminate many repetitive tasks associated with software testing. AI-powered tools can also detect and update obsolete or invalid test cases.
  • Project management: AI project management software brings heightened efficiency and foresight to the project management process by automating data entry and updates, identifying project risks, and dynamically optimizing resource utilization.

What does the future hold?

By empowering software developers to write cleaner code faster, AI-powered code completion tools including ChatGPT, CodeT5, and Polycoder facilitate shorter product release cycles. This will accelerate the evolution of software itself as the auto-coding and test automation tools continue to improve. By casting AI as a tool to improve productivity and decrease development time, future evolution can be steered toward supporting human capabilities rather than replacing them.

As part of this upward spiral, emerging technologies like quantum computing will rely heavily on artificial intelligence to optimize complex quantum algorithms. As a byproduct, quantum computing will symbiotically enhance the power and speed of many AI tools. Software development, manufacturing, education, and many other applications will find generative AI to be a spark that accelerates the pace of innovation. More AI tools will then be required to support the development process, along with more humans with AI knowledge and training.

New opportunities for developers

The evolution of software development will eliminate many traditional roles and tasks, but the changes will yield new opportunities. Machine learning (ML) and NLP are among the important new technologies opening doors for qualified artificial intelligence programmers. Prospective and existing software developers can improve their value and longevity through education and training in these emerging areas. Along with conventional programming languages like JavaScript and Python, AI and ML expertise are also being called upon to support the rapidly expanding internet of things (IoT).

The collaborative potential of AI

83% of tech leaders agree that cross-functional collaboration is crucial for successful software development. Collaboration between developers is essential for methodologies like DevOps that bring operations and development teams out of their silos to improve quality and efficiency. This same spirit of collaboration can lead to a more successful human/AI partnership as developers and project managers weave AI capabilities throughout the process to reduce project fatigue and spur innovation.

Securing our future with AI

Are software developers destined to become the switchboard operators of the 21st century? All signs point to no. Phone services were an ideal candidate for full automation, but successful software development will always rely on uniquely human traits like creativity, ethical sensitivity, and the cross-pollination of ideas. Instead of replacing developers, the speed of innovation engendered by AI is creating a talent vacuum eager to be filled by the next generation of AI and ML specialists.

References:

https://www.forbes.com/sites/ariannajohnson/2023/03/30/which-jobs-will-ai-replace-these-4-industries-will-be-heavily-impacted/?sh=64fecdc5957f

https://www.mckinsey.com/featured-insights/mckinsey-explainers/whats-the-future-of-generative-ai-an-early-view-in-15-charts

https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm

https://www.mdpi.com/2075-4698/11/2/50

https://www.businessinsider.com/software-engineers-tech-panicking-golden-age-over-chatgpt-ai-blind-2023-4

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4419501

https://www.datasciencecentral.com/ais-transformative-role-in-software-testing-and-debugging/#:~:text=AI%20automates%20software%20debugging.,AI%20for%20real%2Dtime%20debugging.

https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/unleashing-developer-productivity-with-generative-ai

https://merge.rocks/blog/the-ethics-of-ai-in-software-development-what-developers-need-to-know

https://www.forbes.com/sites/bernardmarr/2023/03/27/the-intersection-of-ai-and-human-creativity-can-machines-really-be-creative/?sh=f5960ee3dbc4

https://www.functionize.com/automated-testing/ai-testing-revolution-the-implications-of-ml-in-software-testing

https://www.kellton.com/kellton-tech-blog/5-ways-ai-is-improving-software-development

https://www.datanami.com/2023/07/24/quantum-computing-and-ai-a-leap-forward-or-a-distant-dream/#:~:text=Despite%20these%20early%20challenges%2C%20the,that%20classical%20neural%20networks%20cannot

https://brainhub.eu/library/software-developer-age-of-ai

https://www.indeed.com/career-advice/career-development/iot-skills

https://atlassianblog.wpengine.com/wp-content/uploads/2021/10/atlassian_forrester_valueofopencollaboration.pdf

https://lucidspark.com/blog/how-collaboration-improves-devops