Multi-Step Dynamic Ensemble Selection to Estimate Software Effort

ABSTRACT

Software Effort Estimation (SEE) is a foremost concern of software companies in order to successfully develop and deliver software products within a defined budget and time. Many software companies fail to deliver the product on time, either due to problems of over-estimation or under-estimation. In order to aid the decision-making process of the analyst and experts, the paper proposed a multi-step dynamic ensemble selection (MS-DES) approach. The DES works in two steps; I) in order to select the suitable models from the pool of models, which are anticipated to perform best when generating a prediction. II) to predict the labeled discretized effort more accurately. The paper utilized four software effort datasets discretized into labeled effort ranges. The performance of the proposed model is evaluated based on the K nearest neighbor oracle (KNORA) canonical approach to DES and in order to reduce the complexity, filter feature selection techniques are applied to extract the relevant feature set. The proposed feature selection-based MS-DES model outplayed the individual models in predicting labeled effort in terms of confusion metrics parameters with an accuracy of more than 90% with all datasets and the results are validated using the ROC curve.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The datasets analyzed during the current study are available in different repositories: Desharnais dataset is available in Promise repository, http://promise.site.uottawa.ca/SERepository/datasets/desharnais.arff, Maxwell, China, and Kitchenham datasets are available in Zenodo repository their respective web links are; https://doi.org/10.5281/zenodo.268461, https://doi.org/10.5281/zenodo.268446, https://doi.org/10.5281/zenodo.268457.

Additional information

Funding

This research was funded by Horizon Europe (HORIZON) project number 101138678 — ZEBAI