A recent McKinsey study revealed groundbreaking productivity potential from pairing developers with generative AI tools. Test developers saw coding tasks completed up to twice as fast across refactoring, new feature building, and code documentation.
The gains come from generative AI supercharging developers in 4 key areas:
- Expediting manual and repetitive coding work through autocompletion and documentation
- Jump-starting new code drafting with on-demand suggestions
- Accelerating updates to existing code by easing edits
- Enabling developers to tackle unfamiliar challenges with framework guides and snippets
Leading AI coding assistants like GitHub Copilot, TabNine, and Codex allow developers to generate code snippets and entire functions through conversational prompts, drastically accelerating rote programming work. Developers retain oversight to evaluate quality and customize outputs. While focused on Python currently, experts predict advances across languages and platforms. Though optimal use cases differ. Java and C# projects have seen 10-30% shorter timelines leveraging automation for routine changes. Accelerated coding paves the way for faster release cycles, reduces costs, and frees up resources to focus on innovation. But responsible implementation is key amid rising adoption. Organizations must mitigate risks around data privacy, security vulnerabilities, and reputational impacts through governance policies and controls. Upskilling developers on generative AI best practices also improves experience, and retention while maximizing productivity gains. The future is bright for symbiotic human and AI collaboration in software engineering. With disciplined adoption, generative AI unlocks speed, cost savings, and creativity for transformative gains.
So far, we have analyzed how the IT industry is leveraging AI to its advantage. But can we assure that everything described above is true? At Huenei, we incorporated the use of AI tools very early on. Given the promising landscape they offer and the technological revolution they entail, we could not refrain ourselves and had to give it a try. The incorporation of AI into our processes has helped streamline our and our client’s productivity. Through the use of Copilot, the autocomplete tool created by GitHub in partnership with OpenAI, we have managed to make code-writing tasks more efficient. Based on previously generated code, Copilot can autocomplete code lines or blocks. The decision to incorporate it was based on the good metrics achieved, with 40% of its Python suggestions being accepted by developers. It is important to keep in mind that developer intervention will always be necessary to avoid risks due to errors. AI has also assisted us in the process of executing unit tests, saving time and resources. Machine learning algorithms can analyze code and automatically generate test cases quickly, identifying possible scenarios and generating relevant data, reducing manual workload and accelerating the process. We have achieved optimization of unit testing by identifying areas of code prone to errors, allowing us to focus our efforts on critical flows. Similarly, code analysis provides us with recommendations on areas to expand testing coverage. By gathering and preparing test data, we have implemented a model that aligns with existing processes. The constant training and monitoring help guarantee risk mitigation. The results have been excellent. Leveraging intelligence represented an exciting opportunity to enhance the efficiency and quality of software development through automation, increasing the reliability of outcomes and reducing costs of the end product.