The intersection of AI and API Development

Transcript

1. The Intersection of AI and API Development 1
2. An Objective Overview 2
3. Code Automation 3
4. Assisted Documentation 4
5. Detecting Anomalies 5
6. Optimization Insights 6
7. Security Considerations 7
8. Predictive Scaling 8
9. Assisting the Testers 9
10. Integration Suggestions 10
11. Guiding Developers 11
12. Decoding Natural Language 12
13. Managing Dependencies 13
14. Monitoring Metrics 14
15. Versioning Through AI 15
16. In conclusion 16
17. API Lifecycle supported by AI 17
18. Consumer needs 18 Consumer needs describe the discovery sub-process. Consumers search for data in order to use it profitably for the organisation. One goal to fulfil consumer needs is the use of semantic APIs and the creation of a semantic search based on a large language model based on the API definitions of the API initiative. We can achieve this through embeddings in combination with an API.
19. Understanding the needs 19 API providers supported by API enablement teams need a deeper ramp-up in terms of design thinking methodologies. So far, we rely on the use of personas to better understand the consumer base of the API. Based on the consumers, we can draw conclusions about their requirements. To be able to look at these inferences in a meaningful way, the idea of the empathy map will be introduced. To further sharpen the results, the What-How-Why methodology will also be used. In the end, all this leads to a customer journey map. With the help of AI, in particular ChatGPT, all results can be summarised in a mind map for a better overview.
20. First draft of an API 20 When we develop a first draft of an API design, it always seems more important to write down the description directly using a specific definition language. Instead, it is better to rely on a Domain Specific Language (DSL) that describes the models and a corresponding interface either based on a natural language or through paradigms and concepts of a programming language. This leads to a corresponding trained large language model being able to fully support this process step.
21. Review an API 21 With idea from @apihandyman to record the results of a validation as a basis for a possible review in an Excel table. To draw conclusions about how APIs are designed within the organisation. About the evaluations, an AI could be used to provide a corresponding statement about the quality of the design of an API.
22. Implement the API 22 With AI in combination with the AppStore, among other things, we can make it clear to developers at an early stage which framework is suitable for their respective use case based on the description. AI also reacts on the basis of data. Once the framework or technology has been selected, the development process can be supported and driven forward with the help of Github Copilot.
23. Test the API 23 For testing an API or the service, we can use AI in the sense of ChatGPT. The test cases are created based on the respective definition and can be provided in different formats.
24. Deploy the API No support from an AI so far. 24
25. Monitor the API 25 If we think monitoring towards observability, we have reached a point where we can always allow an AI to make decisions based on data. More precisely, the AI checks compliance with KPIs. This results in the decision regarding the maintenance or further development or the so-called sunsetting of an API.
26. Maintain the API The flow can be restarted again based on the decision of the AI. 26
27. Retire the API No need for an AI. The API will be still available in the API Catalogue for historical and continuous improvement reasons. 27
28. 28 References Cover Slide: Photo by Denys Nevozhai on Unsplash Slide 3: Photo by Max Chen on Unsplash Slide 4: Photo by Amy Hirschi on Unsplash Slide 5: Photo by Roger Starnes Sr on Unsplash Slide 6: Photo by Lukas Blazek on Unsplash Slide 7: Photo by Scott Webb on Unsplash Slide 8: Photo by Calum MacAulay on Unsplash Slide 9: Photo by ThisisEngineering RAEng on Unsplash Slide 10: Photo by rizki rama28 on Unsplash Slide 11: Photo by Clay Banks on Unsplash Slide 12: Photo by Annika Gordon on Unsplash Slide 13: Photo by Alvaro Reyes on Unsplash Slide 14: Photo by Luke Chesser on Unsplash
29. codecentric AG Hoher Wall 15 44137 Dortmund Telefon: +49 (0) 151. 10 86 70 74 Daniel Kocot Head of API Experience & Operations daniel.kocot@codecentric.de www.codecentric.de Innovative - Trustful - Competent - Pragmatic 29