  Exploring the impact of change requests applied to a software maintenance
project helps to assess the fault-proneness of the change request to be handled
further, which is perhaps a bug fix or even a new feature demand. In practice,
the major development community stores change requests and related data using
bug tracking systems such as Bugzilla. These data, together with the data
stored in a versioning system, such as Concurrent Versioning Systems, are a
valuable source of information to create descriptions and also can perform
useful analyzes. In our earlier work, we proposed a novel statistical bipartite
weighted graph-based approach to assessing the degree of fault-proneness of the
change request and Change Request artifacts. With the motivation gained from
this model, here we propose a novel strategy that estimates the degree of
fault-proneness of a change request by assessing the impact of a change request
artifact towards fault-proneness that considers the correlation between change
requests artifact as another factor, which is in addition to our earlier
strategy. The proposed model can be titled as Assessing the Fault Proneness
Degree of Change Request Artifacts by estimating the impact of change requests
correlation (DFP-CRC). As stated in our earlier model, the method DFP-CRC also
makes use of information retrieval methods to identify the change request
artifacts of the devised change request. And further evaluates the degree of
fault-proneness of the Change Requests by estimating the correlation between
change requests. The proposed method is evaluated by applying on concurrent
versioning and Change request logs of the production level maintenance project.
