Paper accepted at the ICSME Research Track

We are happy to announce that our paper Static source code metrics and static analysis warnings for fine-grained just-in-time defect prediction was accepted for publication at the Research Track of the 36th International Conference on Software Maintenance and Evolution.The paper reports the results of a large-scale study of 38 open source projects over 15 years of project history. We investigated whether a fine-grained just-in-time defect prediction approach can benefit from additional static source code features as well as static analysis warnings.It includes an evaluation of different feature sets on two machine learning algorithms, an advanced SZZ algorithm to find bug-inducing changes and a specialized cost model for defect prediction.It is the largest study to date that includes static source code metrics and the first study to include static analysis warning density based features in just-in-time defect prediction.