AInalyzer provides in-editor Workflow-Driven AI conversations to improve code comprehension and defect fixing #264
Description
To better understand their code or find defects, novice and experienced front-end developers often rely on external knowledge repositories such Stack Overflow, MDN, or Chat Bots when their current explanation of the software prevents them from making progress. Such barriers present challenges to locate the code presenting some unexpected behavior in their programs, or navigating the design or execution information to pinpoint the code needing changes.
Recently, Chat bots have become an efficient way to gather external explanations to refine developer's understanding of their software. External pages, such as ChatGPT, offer a virtual assistant for insights about code, connecting the dots between design and execution. These interactions have been integrated within IDEs to further improve navigation between code and external explanations. However, Chat Bot explanations may be ineffective. If a developer is not aware that the explanation is incorrect, the developer will be misled further. Furthermore, if the developer did not know what are the facts about design and execution to delimit the problem, the gains from external explanations become losses.
AInalyzer enhances developer-assistant conversations by managing interactions within predetermined workflows. Depending on the nature of the code, the troubleshooting may require information from execution, to which a preset of questions about the APIs involved and their expected runtime information to asses the right path to solve the coding challenge.