The advent of the Petascale era, provides a great opportunity as well as a great challenge for computational science and engineering. Large scale scientific applications need to scale to unprecedented numbers of processing cores and adapt to multi-core architectures with complex memory and network hierarchies in order to fully leverage the computational resources available. In addition to possible human errors, the limit for code writing and the ever-growing complexity of many scientific codes make the development of parallel scientific applications an intimidating task. In addressing these issues, we are developing a generic collaborative problem-solving environment from mathematical abstractions for automating the development of highly scalable and efficient codes that can solve a wide range of scientific problems. In such an environment, application developers, either software engineers or domain experts, can contribute to a code with their expertise at their maximum scale, thus enhance the overall programming productivity and speed up scientific discoveries.