## 3 Related Work  
The rise of intelligent source code completion assistants corresponds with the increasing complexity of software development. These tools enhance code-writing efficiency by predicting and suggesting context-based code snippets. Their influence on software development, encompassing productivity, code quality, and user satisfaction, is under active research. This paper consolidates the existing knowledge, offering a thorough analysis of these assistants' impact. Through an extensive review, we highlight their pros and consand pinpoint areas for future exploration. The software development realm is increasingly exploring machine learning for code generation and retrieval, potentially revolutionising developer approaches to program logic and procedures.  
The authors [18] investigated the impact of machine learning methods for code generation and retrieval on developer productivity and accuracy in the PyCharm IDE. A plugin was developed to facilitate the study, and the developers were asked to complete 14 Python programming tasks with and without the help of the plugin. The results from the qualitative surveys showed positive experiences, but the quantitative results showed inconclusive evidence of increased productivity or code quality. Further analysis identified areas for improvement and when developers preferred code generation or retrieval. The study is the first comprehensive investigation of the topic, and all data and software were released to advance the field further [18].  
The next related work [19] focused on the connection between the productivity benefit of GitHub Copilot, a neural code completion system, and usage measurements from developer activity. The authors collected measurements of the acceptance rate and persistence of completions and found that the acceptance rate correlated better with reported productivity. They suggest that code suggestions in GitHub Copilot should be viewed as a conversation with a chatbot, and they plan to explore this analogy further in future work [19].  
A user study [20] with 24 participants was conducted on the usability of GitHub Copilot. The study investigated users' perceptions, interaction patterns, and coping strategies when the generated code was incorrect. The results showed that although Copilot was preferred by participants for its ability to provide a starting point for programming tasks, it did not necessarily reduce task completion time or increase the success rate. The study also highlighted potential improvements for Copilot, such as better support for understanding and validating the generated code and exploring multiple solutions [20].  
However, prior research has focused primarily on evaluating retrieval accuracy and the overlap of generated code with developer-written code. The actual effect of these methods on the developer workflow remains largely unexamined. While previous studies have delved into the technical aspects and immediate impacts of intelligent source code completion assistants, our article takes a holistic approach by examining the broader landscape of these tools through a systematic literature review. Furthermore, our research emphasises the unique disparity in the perceived value of these assistants between experienced and less experienced developers, shedding light on the nuanced relationship between developer experience and the utility of intelligent assistants.