Abstract
The paper presents an approach, namely iMacPro, to recommend developers who are most likely to implement incoming change requests. iMacPro amalgamates the textual similarity between the given change request and source code, change proneness information, authors, and maintainers of a software system. Latent Semantic Indexing (LSI) and a lightweight analysis of source code, and its commits from the software repository, are used. The basic premise of iMacPro is that the authors and maintainers of the relevant source code, which is change prone, to a given change request are most likely to best assist with its resolution. iMacPro unifies these sources in a unique way to perform its task, which was not investigated and reported in the literature previously.
An empirical study on three open source systems, ArgoUML, JabRef, and jEdit , was conducted to assess the effectiveness of iMacPro. A number of change requests from these systems were used in the evaluated benchmark. Recall values for top one, five, and ten recommended developers are reported. Furthermore, a comparative study with a previous approach that uses the source-code authorship information for developer recommendation was performed. Results show that iMacPro could provide recall gains from 30% to 180% over its subjected competitor with statistical significance.