Navigating the Risks and Rewards of AI in Software Development

AI in software development is still a cautionary tale. Generative AI holds enormous potential to enhance the efficiency and productivity of software developers – but not without associated risks. Managing the expectations and risks of AI in software development is a hot topic after recent attempts at AI coders were met with a mixed response from the community.

As a software development company, our team at Pegasus One has dabbled with AI for writing code – or using it for assistance – and have come across some benefits and some glaring issues. We will try and cover some of them here today

Let us start with the risks.

Understanding the risks of using AI for software development

  • Quality: During our tests, one of the major concerns that we had was regarding the quality of the code produced by AI. How AI learns to code is a factor of this concern to begin with. Generally, AI models are trained on public data sets – data that is freely available. And for learning to code, there is no place like Stack Overflow etc. The issue lies in the fact that not all content on Stack Overflow is of high quality. Many a times, the code provided by contributors on Stack Overflow can be written for the sake of getting around a problem – but not in the most artistic manner. At such platforms, the AI learning happens using lower quality instructions than what a proficient software developer would write, and as a result generates lower quality code which lacks precision and is often riddled with errors.
  • Consistency: During our testing, we realized that the AI suggested widely different approaches to solving a problem – for the same prompt. This should not be an issue if the solution provided each time was at a similar level of acceptance. What we found out was while sometimes the solutions provided were well optimized, to the point and similar to what an experienced developer would have done, many times it gave results with bloated, unnecessarily complicated code that could have been done in a simpler fashion. This inconsistency, if unresolved, would often lead to bloated end products that are not well optimized for time complexity and have a much higher overhead than acceptable.
  • Risks to Intellectual Property: Most of the popular AI tools used today for generation of code can and do use the generated content (and the prompts) for training. Unless you are sure that none of your prompts/code is being used to train the models, there is a risk that your proprietary information ends up with someone else – and is a major concern especially for organizations where Intellectual Properties hold immense competitive and monetary value.
  • Overestimating its capabilities: The AI hype cycle is in full swing right now. It is everywhere – enterprise software, mobile devices, wearable AI assistants, automobiles. While it definitely holds a lot of promise, much of what AI is capable of, at least currently, holds limited potential. It has major flaws – hallucinations, errors and more. Right now, we should think of it as another tool in our arsenal to help us create faster, and sometimes better, solutions. Uncoupled from a human to oversee the accuracy of an AI produced content/code/product, it will almost certainly always produce inferior end results. There is still some time till we can confidently say that AI can replace a human developer, but that time certainly is a little far off.

Risks aside, what are the rewards of using AI for code generation?

  • Productivity: It is absolutely a delight to watch AI generate code in seconds. Used well, this capability of generating code fast saw us improve our output 10x, easily (we never use AI for generating client projects – unless explicitly specified).AI is also great at pointing out bugs – something that human teams take a significantly longer time to achieve. These rewards of using AI for development end up saving a lot of time for software development teams – freeing it up for tasks that an AI is not the best fit: problem-solving and innovation.
  • Cost of development: Less hours spent on generating code, debugging, and testing (excellent area for AI automation) means a lot of man-hours are saved, which result in direct savings for the client.
  • Rapid delivery: 10x output leads to faster delivery of MVPs and in-turn faster time to market. In many situations, this speed is what makes a project successful, helping clients get a competitive edge by being the first to market.
  • A tool for the mind: Using AI to catapult human creativity that is driven by data allows a software development team to tackle complex problems faster. Sometimes, you are limited by the on-hand resources and their knowledge, AI fills that gap and enables you to overcome your teams/personal limitations faster.

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