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Leverage generative AI for faster software development

In the world of software development, speed of delivery is increasingly important.

As noted in the recent Gartner report, How can generative AI tools speed up software delivery, software engineering leaders are being presented with a new solution to speed up software delivery: generative AI tools! 

Whilst rapidly becoming something of a buzzword in tech circles, generative AI tools offer a unique opportunity for teams to streamline their processes, reduce repetitive tasks, and speed up release cycles.

But how can software engineering leaders harness the power of generative AI to optimise their teams' delivery speed? Let's explore the priorities for Software Development Leaders outlined in the report.

Introducing Generative AI Tools for Automation

One of the main challenges faced by software engineering leaders is reducing lead times, i.e. the time between a feature request and its completion. 

According to a poll published by Gartner, 52% of software engineering leaders anticipate their organisations will be adopting generative AI to build software. The report also identifies several key areas where AI augmentation can significantly reduce lead times throughout the software development cycle.

How generative AI can help speed up different phases of software development:

- Plan Phase

High-level product ideas can be transformed into detailed user stories with the help of prompts generated by AI. AI can also aid in prioritising backlogs based on contextual data and data-driven metrics, helping to enhance planning efficiency.

- Create Phase

Generative AI can be used to automate the generation of boilerplate code, unit tests, code structure guidelines, and optimised code comments. By doing so, developers can save time and reduce manual effort in these repetitive tasks, allowing them to concentrate on more critical aspects of software development.

- Verify Phase 

AI can be used to detect and rectify code errors, generate test plans from user stories, and create unit and integration tests. Leveraging generative AI to identify potential security vulnerabilities and enhance application reliability by analysing large data sets such as access logs is also a great way to cut out on repetitive work!

By integrating Generative AI into these critical phases, software engineering leaders can significantly improve efficiency, reducing lead times for feature delivery without compromising on quality! 

Mitigating Risks and Ensuring Quality

While Generative AI tools promise accelerated delivery, it's essential software engineering leaders be aware of potential risks.

Some of these risks include so-called "hallucinations" where AI-generated content may be incorrect; an increased likelihood of introducing defects due to over-reliance on AI; and intellectual property concerns related to copyrighted code and data supplied to AI models.

To navigate these risks, software engineering leaders should:

  • Implement quality control practices to ensure the accuracy and security of AI-generated code.
  • Combine AI-generated code with manual review processes for comprehensive code verification.
  • Anticipate and address intellectual property risks associated with AI model providers.

Overall, generative AI tools offer an exciting avenue for software engineering leaders to accelerate software delivery. 

By strategically incorporating these tools to automate repetitive tasks and actively addressing potential associated risks, leaders can lead their teams toward faster, more efficient, and higher-quality software releases. 

The future of software development is here, and software engineering leaders are well-positioned to capitalise on its potential! 

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