HBS-CRA: scaling impact of change request towards fault proneness: defining a heuristic and biases scale (HBS) of change request artifacts (CRA)

Accurately calculating the impact of existing change requests is vital for estimating the probability of fault occurrence in future change requests. For a new request like bug fixing, effectiveness in current change requests is required. In a real-time scenario, bug trackers are deployed to save change requests and their associated information. The trackers and associated information are saved in CVS version control systems and these systems assist programmers to carry out multiple analytical functions and generating descriptions. In our earlier works, we devised the set of change request artifacts and also proposed novel statistical bipartite weighted graphical models to evaluate DFP degree of future change requests. With the motivation gained from this model, here we propose a novel strategy that estimates the DFP of the request by assessing the impact of a change request artifact towards fault-proneness that considers the correlation between code blocks as another factor, which is in addition to our earlier strategy. A novel heuristic and biases scale to evaluate the effectiveness of change request for DFP is devised here in this paper that titled as “Defining a Heuristic and Biases Scale (HBS) of Change Request Artifacts (CRA)”, in short HBS-CRA. The devised model makes use of information retrieval methods to identify the change request artifacts of the request. In addition, it also checks for DFP scope through HBS-CRA. The HBS-CRA is empirically assessed by applying on concurrent versioning and Change request logs of the production level maintenance project.