The paper presents an approach, namely iHDev, to recommend developers who are most likely to implement incoming change requests. The basic premise of iHDev is that the developers who interacted with the source code relevant to a given change request are most likely to best assist with its resolution. A machine-learning technique is first used to locate source code entities relevant to the textual description of a given change request. iHDev then mines interaction trails (i.e., Mylyn sessions) associated with these source code entities to recommend a ranked list of developers. iHDev integrates the interaction trails in a unique way to perform its task, which was not investigated previously.

An empirical study on open source systems Mylyn and Eclipse Project was conducted to assess the effectiveness of iHDev. A number of change requests were used in the evaluated benchmark. Recall for top one to five recommended developers and Mean Reciprocal Rank (MRR) values are reported. Furthermore, a comparative study with two previous approaches that use commit histories and/or the source code authorship information for developer recommendation was performed. Results show that iHDev could provide a recall gain of up to 127.27% with equivalent or improved MRR values by up to 112.5%.