Swimm today announced it has added a chat tool that enables developers to use natural language to surface insights into code.
Company CEO Oren Toledano said the /ask Swimm tool makes it simpler to launch queries that enable developers to better understand how code was constructed.
The /ask Swimm tool aggregates documentation along with other related data to surface factors that are not evident in the code itself, such as documentation of business decisions, product design considerations and decisions concerning why specific architectural choices were made. It automatically captures and updates code-related knowledge to provide a continuous feedback loop as code, documentation, files and repositories are created and updated to provide deeper insights into code that goes beyond what might have been actually documented.
The overall goal is to provide a mechanism that enables developers to understand how and why code was constructed within the context of an integrated development environment (IDE), said Toledano.
Swimm has previously made available a platform that uses generative AI platform to create a static analysis of documentation. That capability should make it simpler for organizations to track documentation at a time when generative AI platforms such as ChatGPT are exponentially increasing the amount of code being written. In theory, those platforms should be able to also create documentation for that code, but there will still be a need for tools to track and analyze it.
In the longer term, Swimm plans to continue to extend its usage of generative AI to provide deeper levels of insights across entire applications and software ecosystems to address that challenge, noted Toledano.
In the short term, however, it’s clear that AI is making developers more productive than ever. In fact, the volume of code being generated might soon overwhelm existing DevOps pipelines and workflows. Most DevOps teams will need to revamp those workflows as the pace at which more applications than ever are being developed and deployed faster than ever.
It’s still early days as far as generative AI adoption is concerned, but it’s clear many developers are already using it to write code. How much of that code makes it into a production environment is difficult to determine, but a lot of that code is going to be of varying quality. A general-purpose AI platform such as ChatGPT was trained using code collected from all across the web. The code generated by these platforms is only as good as the examples used to train it, all of which were created by human developers who may have made mistakes, such as including code that has known vulnerabilities.
Regardless of whether code was generated by a machine or a human, the probability that documentation will need to be analyzed as part of a code review process is always high. The challenge and the opportunity is to determine the best way to apply AI to a longstanding challenge that is now being exacerbated by the existence of AI tools that, with each passing day, are only becoming more accessible to developers.
