Heterogeneous Ensemble Model to Optimize Software Effort Estimation Accuracy

Abstract

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