Leveraging AI to Reduce Developer Fatigue and Burnout
Tags: developers, psychology, ai, development
Published: 2024-03-01 18:43:53

In the constantly changing world of software development, mental fatigue and burnout are an all-too-common reality for many developers. Some of the most common culprits? A continuous amount of routine tasks, excessive mental demands, interruptions, frequent context switching, and a multitude of other distractions that developers must navigate daily. It's no wonder that developers are frequently mentally exhausted at the end of the workday.<br /><br />


### The Impact of Context Switching

Context switching, a notorious energy drain, can be mitigated with generative AI integration into the IDE. The switch often starts with needing to get help with a small question, followed by a Google search. Then 5, 10, 30 minutes later an answer is found and now the developer has to figure out where they had stopped and exactly what problem they were trying to solve in the first place. Research suggests that it takes approximately 15 minutes for a developer to regain the same level of focus they had before an interruption<sup>[1]</sup>. For complex tasks, the lag between interruptions and resuming work has shown to take over 30 minutes<sup>[2]</sup>. Switching back and forth from a web browser to the IDE hinders the developer’s ability to remain in the mindset needed to solve their current problem.<br /><br />

### AI as a Development Tool

AI tools, such as ChatGPT, can also be used to alleviate some of this cognitive drain by reducing the need to sift through multiple documentation sources, Stack Overflow threads, and blogs. Developers can also avoid the barrage of Google marketing, excessive ads, and SEO-skewed results. While AI doesn't eliminate the need for research, it provides a focused framework for developers to seek out answers. It doesn’t always provide the best solution, but it can provide some major guidance while you seek information elsewhere.

It’s still the responsibility of the developer to ensure that their code is functional, clean, and able to integrate into the existing code base. The expertise on how to implement the suggestions by AI still falls on the developer. Using AI to help develop doesn’t remove the need for writing good tests. Tests are a necessary and tedious step to writing robust code. Chat GPT can help with test coverage by reading through code, such as individual functions and classes, and generating comprehensive test cases. Developers don’t have to spend so much time creating mock data either. Have a test that needs mock data? Give ChatGPT the model and let it generate the data. This helps eliminate another context switch and allows the developer to stay focused on the creative, challenging, and rewarding aspects of software development.<br /><br />

### Confidentiality and AI

For organizations concerned about confidentiality, private instances of ChatGPT can be developed to protect sensitive information, offering the flexibility to leverage AI without compromising privacy. This can ensure that code stays within the company and isn’t sent out as a source of learning for AI algorithms. For AI tools where data is released for learning, such as the public-facing ChatGPT, developers need to be aware that they may need to genericize more of their prompts to protect company-owned data and remove any identifiable information.<br /><br />


### Conclusion

It's crucial to remember that AI wasn’t created as a replacement for developers. It was created by developers as a tool to simplify our lives, enhance our code, and to help reduce the amount of distractions and monotonous work that take up so much of our time. By embracing AI, we can shift our mental energy from being spent working through the tedious parts of software development back onto creating fun, innovative solutions.<br /><br /><br /> 
  
  

  
#### References

[1] R. Solingen, E. Berghout, and F. van Latum, "Interrupts: Just a Minute Never Is," IEEE Software, vol. 15, no. 5, pp. 97-103, Sept.-Oct. 1998, doi: 10.1109/52.714843.

[2] C. Parnin and S. Rugaber, "Resumption Strategies for Interrupted Programming Tasks," Software Quality Control, vol. 19, pp. 5-34, 2011, doi: 10.1109/ICPC.2009.5090030.<br />


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