Time-Aware Models for Software Effort Estimation
This study addresses the problem of improving software effort estimation accuracy for project managers by incorporating temporal information, representing an incremental improvement to existing methods.
This paper investigates the impact of incorporating project start and completion dates into software effort estimation (SEE) models. The authors applied two time-aware modeling approaches to two public datasets and compared them against three baseline models, demonstrating the feasibility of building accurate time-aware models that differ structurally from non-time-aware models.
It seems logical to assert that the dynamic nature of software engineering practice would mean that software effort estimation (SEE) modelling should take into account project start and completion dates. That is, we should build models for future projects based only on data from completed projects; and we should prefer data from recent similar projects over data from older similar projects. Research in SEE modelling generally ignores these recommendations. In this study two different model development approaches that take project timing into account are applied to two publicly available datasets and the outcomes are compared to those drawn from three baseline (non-time-aware) models. Our results indicate: that it is feasible to build accurate effort estimation models using project timing information; that the models differ from those built without considering time, in terms of the parameters included and their weightings; and that there is no statistical significance difference as to which of the two model building approaches is superior in terms of accuracy.