This paper is investigating if it is possible to predict source code qualitybased on static analysis and machine learning. The proposed approachincludes a plugin in Eclipse, uses a combination of peer review/humanrating, static code analysis, and classification methods. As training data,public data and student hand-ins in programming are used. Based onthis training data, new and uninspected source code can be accuratelyclassified as “well written” or “badly written”. This is a step towardsfeedback in an interactive environment without peer assessment
