English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

With the prevalence of imperfect but capable LLMs in software development, itbecomes increasingly important to develop debugging skills -- to formhypotheses about the source of error in both human codes and codes from theirAI pair programmers. Despite the necessity, hypothesis construction indebugging is rarely taught. In this work, we explore whether LLMs can be usedto train novices on hypothesis construction, by designing a theoreticallymotivated, LLM-augmented tutor -- HypoCompass. HypoCompass uses LLMs togenerate rich training materials guided by learning principles and presentsthem in a learning-by-teaching environment, where LLMs act as students whowrite bugs, and human novices play the role of Teaching Assistants to helpdebug and fix the code. Evaluations show that \sysname makes high-qualitytraining materials four times more efficiently than humans and bringssignificant learning gain: 19 novices improved their pre-to-post testperformances by 12%, with a reduced completion time of 14%.

HypoCompass: Large-Language-Model-based Tutor for Hypothesis Construction in Debugging for Novices