AI-Assisted Performance Feedback in API Programming

- PDT
Workshop Stage 1
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Malith Jayasinghe
WSO2, VP of Research

Malith is a computer scientist, author, blogger, and software architect with more than 15 years of industry experience in designing, developing, and optimizing complex software systems.

Prior to joining WSO2, he worked for multinational companies in various capacities which included Research Engineer, Technical Lead, Software Architect, and Product Specialist.

Malith is a frequent speaker at developer and computer science conferences and meetups. He's published articles in journals such as IEEE Transactions on Parallel and Distributed Systems (TPDS) and Journal of Parallel and Distributed Computing (JPDC) and presented work at conferences including IEEE Cluster, IEEE NCA, ACM DEBS and IEEE ICWE. Malith is the co-author of the book Performance of Web Services.

Malith understands the value of continuous innovation to stay relevant in a rapidly transforming market. In his current role as VP of research, he leads WSO2 research which primarily focuses on carrying out cutting-edge research in distributed systems, cloud computing, performance engineering, and machine learning.

He holds a Ph.D. in Computer Science from the RMIT University, Australia. Bachelor of Engineering (Hons) in Computer Systems Engineering from Massey University, New Zealand, and a Certificate in Leadership and Communication from Curtin University, Australia.


As businesses continue to expose information through APIs, API programming has grown significantly and the number of API endpoints available has increased by leaps and bounds. An integration consuming a set of such APIs needs to adhere to the agreed SLAs with the customers. In the era of cloud integration platforms, making sure that your cloud-based integration is up to scratch in performance is not simple.

Integration-based development increases the risk of performance mistakes in the code compared to traditional programs that do not depend on external services. Since developers combine multiple services or APIs with unknown performance characteristics, these mistakes are usually missed out during development. With the help of artificial intelligence (AI), integrated development environments (IDE) can take up the burden of helping engineers to write performant code.

In this session, I will talk about how to use both AI and theoretical performance models to provide accurate performance forecasts for API integrations. I will demonstrate how this approach can be useful for inexperienced developers to write performant code.