Based on our interdisciplinary background, we propose to radically change research and development methodology as well as publication model in computer engineering that favors collaborative discovery, systematization, sharing and reuse of knowledge. Motivated by physics, biology and AI sciences, we developed the first version of public repository and infrastructure (cTuning.org) to allow researchers share data (applications, data sets, codelets and architecture descriptions), modules (classification, predictive modeling, run-time adaptation) and statistics about behavior of computer systems manually or automatically. Having common infrastructure and repository allows users to quickly reproduce and validate existing results, and focus their effort on novel approaches combined with with data mining, classification and predictive modeling rather than spending considerable effort on building new tools with already existing functionality or using some ad-hoc tuning heuristics. It will also allow conferences and journals to favor publications that can be collaboratively validated by the community.