Generative AI tools cut software task time by up to 70%: Study

A Ness and Zinnov report finds Generative AI tools like Copilot can reduce software development task times by up to 70% for code updates and 48% for senior engineers, significantly boosting productivity and improving team engagement.

The adoption of Generative Artificial Intelligence (AI) in software development can significantly improve productivity and reduce task completion time, according to a report by Ness, a digital engineering services company, and Zinnov, a global management consulting firm.

The report highlighted that Generative AI tools such as Copilot and CodeWhisperer have the potential to transform software engineering productivity, particularly in routine development tasks. It stated, "Generative AI (GAI) has a significant impact on repeatable sustenance activities and reducing knowledge barriers... 70% reduction in task completion time for existing code updates..... 48% reduction in task completion time for senior engineers."

Ness and Zinnov conducted a detailed analysis of more than 100 software engineers across various use cases and development environments to assess the real-world impact of Generative AI in software development.

Significant Productivity Gains Reported

According to the findings of the study, Generative AI has the potential to significantly reduce the time required to complete certain development tasks.

One of the key outcomes of the study showed that task completion time for existing code updates can be reduced by as much as 70 per cent when developers use Generative AI tools. This indicates that AI can be particularly useful in repetitive coding activities and maintenance work.

Impact Varies by Experience and Complexity

The report also noted that Generative AI tools can improve productivity among engineers with different levels of experience. The study found that senior engineers experienced a 48 per cent reduction in task completion time when using these AI tools.

However, the report added that the impact of Generative AI may vary depending on several factors, such as the experience level of engineers, the complexity of the coding task, and the development environment.

In cases where coding tasks are highly complex, the productivity improvement from AI tools appears to be more limited. The study observed that high code complexity environments saw around a 10 per cent reduction in task completion time, suggesting that skilled engineers will continue to play a crucial role in complex software development.

Improved Knowledge Sharing and Collaboration

The report further highlighted that the use of Generative AI can also improve knowledge sharing and collaboration within development teams. According to the study, around 70 per cent of engineers reported improved engagement while working with Generative AI tools. The report noted that such tools can reduce knowledge barriers between teams and help developers work more effectively in distributed global teams.

Study Methodology

Ness used its proprietary Matrix platform, a dynamic data-driven engineering platform, to monitor key engineering performance indicators such as quality, productivity, responsiveness, and code quality during the study.

Conclusion

The report concluded that Generative AI has strong transformative potential in software engineering if used appropriately, but its overall impact will depend on factors such as engineer seniority, task type, and the complexity of the code involved. (ANI)

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