  In recent times, a plethora of Large Code Generation Models (LCGMs) have been
proposed, showcasing significant potential in assisting developers with complex
programming tasks. Benchmarking LCGMs necessitates the creation of a set of
diverse programming problems, and each problem comprises the prompt (including
the task description), canonical solution, and test inputs. The existing
methods for constructing such a problem set can be categorized into two main
types: manual methods and perturbation-based methods. However, manual methods
demand high effort and lack scalability, while also risking data integrity due
to LCGMs' potentially contaminated data collection, and perturbation-based
approaches mainly generate semantically homogeneous problems with the same
canonical solutions and introduce typos that can be easily auto-corrected by
IDE, making them ineffective and unrealistic. In this work, we propose the idea
of programming problem merging (PPM) and provide two implementation of this
idea, we utilize our tool on two widely-used datasets and compare it against
nine baseline methods using eight code generation models. The results
demonstrate the effectiveness of our tool in generating more challenging,
diverse, and natural programming problems, comparing to the baselines.
