This research identifies factors that impact software maintenance effort by exploring the decision-making process of expert estimators of corrective maintenance projects by using qualitative methods to identify the factors that they use in deriving estimates. We implement a technique called causal mapping, which allows us to identify the cognitive links between the information that estimators use, and the estimates that they produce based on that information. Results suggest that a total of 17 factors may be relevant for corrective maintenance effort estimation, covering constructs related to developers, code, defects, and environment. When these factors are rank-ordered, they demonstrate that some of the factors that have greater influence on corrective maintenance estimation, as expressed by expert estimators, are very specific to corrective maintenance and not generally observed in popular software estimation or maintenance estimation models. This line of research aims at addressing the limitations of existing maintenance estimation models that do not incorporate a number of soft factors, thus, achieving less accurate estimates than human experts.
展开▼
