SAP Introduces AI-Assisted ABAP Coding-Tool to Boost Cloud Developer Productivity

The move to infuse AI into ABAP, coinciding with the language's 40th anniversary, emphasizes SAP's effort to maintain the relevance of its well-established programming standard.

SAP has announced the introduction of artificial intelligence (AI)-assisted coding tools for its cloud-based application development environments. The announcement, made during the SAP TechEd developer conference, indicates SAP's commitment to improving development productivity through the use of advanced AI capabilities.

Cloud Strides in Development

The newly introduced tools include SAP Build Code, which integrates AI into the SAP Build low-code development . Specifically, AI co-pilot Joule is designed to assist developers in creating data models, application logic, and test scripts in well-known programming languages such as JavaScript and Java. Moreover, in an effort to support SAP's unique programming language, the ABAP Cloud Environment is also set to benefit from AI-assisted generative AI coding. These tools are currently in preview and expected to launch next year, enhancing the cloud development experience on SAP's Business Technology Platform (BTP).

The move to infuse AI into ABAP, coinciding with the language's 40th anniversary, emphasizes SAP's effort to maintain the relevance of its well-established programming standard in the modern cloud era. ABAP Cloud Environment, which is the successor to the former Embedded Steampunk, serves as the dedicated development tool within the cloud for SAP-specific applications.

Chief Technology Officer Juergen Mueller explained that the unified lobby in BTP facilitates smoother collaboration between citizen developers and professional developers, with capabilities improving overall productivity. Moreover, these advancements in Build Code and ABAP Cloud Environment are key to integrating apps and extensions with SAP's enterprise resource planning system, S/4HANA, keeping in line with SAP's objective of a “clean core.”

In parallel to the developments in AI-assisted coding, SAP plans to launch a vector engine for its HANA in-memory database in early 2024, promising enhancements in similarity search and content-based filtering. This initiative is part of a more comprehensive movement in the database market to support vector search, as seen with other mainstream and specialized databases.

Developer Sentiments and Future Prospects

Despite what appears to be a forward-thinking initiative, some developers have expressed concerns, according to The Register. The crux of the issue lies in the fact that a significant portion of SAP development work is still conducted on on-premises systems. For many professionals working on migrating and adapting these systems to the cloud, the cloud-only approach of the newly announced tools may not provide immediate benefits.

Experts like Jelena Perfiljeva and Tobias Hofmann have commented on the potentially limited applicability of the cloud-only tools and the delay in general availability, respectively. They are emphasizing a need for tools that could facilitate the transition from on-premises development to a cloud-first approach. Notwithstanding the hesitations, SAP's ambition to drive innovation in AI-powered coding tools aligns with industry trends where companies like MongoDB, AWS, and are also integrating AI into their development environments.

Holger Mueller, Vice President and Principal Analyst at Constellation Research, pointed out that focusing on ABAP first would have been more in line with customer needs, given the prevalence of ABAP code within the SAP user base. Nevertheless, SAP appears determined to help its customers pivot from on-premises to cloud-based systems, even though the approach and specific tooling for achieving this remain subjects for further discussion and development.

The Era of AI Coding Tools

  • A tool that got an equity investment from Microsoft is Builder.ai, an AI software firm that offers Natasha AI product manager through Microsoft Teams. Builder.ai and  are two different types of AI-powered tools for software development. Builder.ai is a no-code platform that lets users make apps by picking from various templates and features, without writing any code.
  • also partnered with Replit to offer Ghostwriter, an AI tool that helps developers write code. The partnership also gives Replit developers access to Google Cloud and vice versa. Moreover, Google brought code generation and debugging to its Bard AI chatbot. Users can write their coding questions or requests in natural language, and Bard will generate multiple drafts of possible responses for them to pick from.
  • Amazon launched CodeWhisperer, a free AI tool that competes with Copilot. It works with , JavaScript, and Java languages and integrates with popular IDEs like PyCharm and Visual Studio Code. It helps users write code faster and easier. CodeWhisperer is integrated with AWS services and tools, such as Lambda, CloudFormation, and Amplify.
  • In May, Meta introduced CodeCompose, an AI-powered tool that offers code suggestions for various languages including Python, as developers type in Integrated Development Environments (IDEs) like VS Code. The tool can utilize its understanding of the surrounding code to provide enhanced suggestions.
  • Chinese company Baidu is also competing in this space with its own coding AI. Comate is compatible with mainstream Integrated Development Environment (IDE) frameworks and supports more than 30 programming languages, with a strong emphasis on C/C++, Python, and Java.
  • In July, Stack Overflow introduced its OverflowAI coding assistantOverflowAI is a web-based tool that allows users to input natural language queries and get code snippets generated by a deep learning model trained on millions of  posts and other sources. The platform supports various programming languages, such as Python, C#, Java, and SQL.
  • has been positioning itself as a leader in the AI market and the company's SafeCoder reflects the ongoing success. Launched in August, the tool ensures that code remains within the Virtual Private Cloud (VPC) during both training and inference stages. The design of SafeCoder allows for on-premises deployment, giving enterprises ownership of their code, similar to a personalized GitHub Copilot.