Generating Examples From CLI Usage: Can Transformers Help?
This addresses the issue for programmers who rely on accurate examples to write efficient and secure software, though it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of outdated software documentation by developing a system that uses machine learning on telemetry data and documentation corpora to generate up-to-date examples, achieving 100% coverage for used functionalities and reducing documentation-related PRs by over 68%.
Continuous evolution in modern software often causes documentation, tutorials, and examples to be out of sync with changing interfaces and frameworks. Relying on outdated documentation and examples can lead programs to fail or be less efficient or even less secure. In response, programmers need to regularly turn to other resources on the web such as StackOverflow for examples to guide them in writing software. We recognize that this inconvenient, error-prone, and expensive process can be improved by using machine learning applied to software usage data. In this paper, we present our practical system which uses machine learning on large-scale telemetry data and documentation corpora, generating appropriate and complex examples that can be used to improve documentation. We discuss both feature-based and transformer-based machine learning approaches and demonstrate that our system achieves 100% coverage for the used functionalities in the product, providing up-to-date examples upon every release and reduces the numbers of PRs submitted by software owners writing and editing documentation by >68%. We also share valuable lessons learnt during the 3 years that our production quality system has been deployed for Azure Cloud Command Line Interface (Azure CLI).