Programming by voice can be a viable option for programmers who suffer from physical disabilities such as Repetitive Stress Injury (RSI), but study shows there are still challenges and limitations in the area, from the requirement of syntax-specific language to inaccuracy of speech recognition. We aim to address the potential benefit and performance of gradually integrating ChatGPT with its versions 3.5/4.0 and how to best communicate (speech optimization) to see if the area of programming by voice can be improved in terms of reduced vocal and cognitive load. We present a tool named ChatGPT VCG which contains two approaches, a traditional keyword interpretation approach using the Serenade tool as a code generator and a direct code generation approach using ChatGPT to generate code directly. Using test tasks written in Java, and the Serenade tool as a base measurement, the two approaches are compared and measurements such as the number of words and code characters generated are collected and analyzed. The result indicates that integrating ChatGPT allows the user to circumvent the required syntax-specific language. The direct code generation using ChatGPT 4.0 performed the best with a 4.2 times reduction in the required number of words compared to standard Serenade, while the keyword-based approach at worst shows a 14% increase. Speech optimization shows performance can be further increased by reducing or removing superfluous grammar and instead only providing the relevant information in commands. The study concludes that integration of ChatGPT can improve performance, and the speech can be optimized to reduce the number of words, but often at the cost of a conversational speech pattern.

Previous research has shown that the reactive paradigm is suitable for programming by voice, due to its frequent use of expressions used in common English. However, the software used in the previous research (Talon) does not support reactive programming. To our knowledge, no other programming-by-voice software has built-in support for RxJava. The main focus of this study is the development and testing of a vocal programming environment that recognizes RxJava operators and commands. This is done by writing custom scripts that form a RxJava-adapter for a software called Serenade. The purpose of the adapter is to determine to what degree customization can improve the programming-by-voice experience, in terms of reduced cognitive and vocal load. The testing process also reveals challenges related to programming-by-voice in reactive Java, as well as challenges related to creating custom voice commands for Serenade. The results indicate that Serenade is superior to Talon, requiring on average 45\% fewer words to produce the same code. When the default version of Serenade is compared to the custom version with custom voice commands, the improvement is 19\%. The study also concludes that there are many challenges facing speech recognition when used in a programming environment. The custom script feature in Serenade, while still having some limitations, is advanced and allows the user to create complex custom voice commands.