Background Several prediction models have been proposed in the literature using different techniques obtaining different results in different contexts. The need for accurate effort predictions for projects is one of the most critical and complex issues in the software industry. The automated selection and the combination of techniques in alternative ways could improve the overall accuracy of the prediction models. Objectives In this study, we validate an automated genetic framework, and then conduct a sensitivity analysis across different genetic configurations. Following is the comparison of the framework with a baseline random guessing and an exhaustive framework. Lastly, we investigate the performance results of the best learning schemes. Methods In total, six hundred learning schemes that include the combination of eight data preprocessors, five attribute selectors and fifteen modeling techniques represent our search space. The genetic framework, through the elitism technique, selects the best learning schemes automatically. The best learning scheme in this context means the combination of data preprocessing + attribute selection + learning algorithm with the highest coefficient correlation possible. The selected learning schemes are applied to eight datasets extracted from the ISBSG R12 Dataset. Results The genetic framework performs as good as an exhaustive framework. The analysis of the standardized accuracy (SA) measure revealed that all best learning schemes selected by the genetic framework outperforms the baseline random guessing by 45–80%. The sensitivity analysis confirms the stability between different genetic configurations. Conclusions The genetic framework is stable, performs better than a random guessing approach, and is as good as an exhaustive framework. Our results confirm previous ones in the field, simple regression techniques with transformations could perform as well as nonlinear techniques, and ensembles of learning machines techniques such as SMO, M5P or M5R could optimize effort predictions.

The Balanced Scorecard (BSC) has been used for performance measurement and strategy management for many years. This literature review analyzed studies about the implementation of the BSC in small companies, identifying implementation cases of BSC reported in small enterprises, factors of influence of small enterprises to implement the BSC model, main stages of implementation of the BSC in small enterprises, and future research lines. As a result 23 papers, 12 academic theses and 8 industry reports were identified. Our results shown that small enterprises can take advantage of its specific characteristics to implement the BSC and the implementation process in small companies may differ from the original BSC implementation process. There is little literature on the implementation of the BSC in small businesses, thus it is necessary additional empirical evidence on the effectiveness of CMI implementations in small businesses.