Heterogeneous Ensemble Model to Optimize Software Effort Estimation Accuracy

The software industry has rapidly expanded, and development is now essential to the success of many multinational corporations. demand for complex systems also dramatically increased. Effective essential, given limitations resources such as money, time, labor. cost effort calculations affect process client needs. Project failure was usually caused by errors in job estimating. Underestimating could have severe repercussions, if project’s budget ends up being more than anticipated. overruns, on other hand, can a detrimental impact how successfully projects are finished. Researchers experts field investigating ways keep management productivity at high levels. However, stand-alone estimation not produced any noteworthy research results. Over last decade, standalone estimating models revealed inadequacies. Literature suggests opting ensemble models, would yield better results compared models. We proposed heterogeneous (EEE) model this research. comprises 1) Use Case Point, 2) Expert Judgment (EJ) 3)Artificial neural network (ANN). Finally, using linear combination rules, each unique base combined. applied our benchmark dataset i.e. ISBSG three different variations avoid biasness. Furthermore, trained were use-cases cross-validation. findings study demonstrated that, comparison estimate strategies, technique gave