Is One Hyperparameter Optimizer Enough?

Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics. To address this gap in the literature, this paper applied a range of hyperparameter optimizers (grid search, random search, differential evolution, and Bayesian optimization) to defect prediction problem. Surprisingly, no hyperparameter optimizer was observed to be `best' and, for one of the two evaluation measures studied here (F-measure), hyperparameter optimization, in 50\% cases, was no better than using default configurations. We conclude that hyperparameter optimization is more nuanced than previously believed. While such optimization can certainly lead to large improvements in the performance of classifiers used in software analytics, it remains to be seen which specific optimizers should be applied to a new dataset.Comment: 7 pages, 2 columns, accepted for SWAN1

Measuring Defect Datasets Sensitivity to Attributes Variation

The study of the correlation between software project and product attributes and its modules quality status (faulty or not) is the subject of several research papers in the software testing and maintenance fields. In this paper, a tool is built to change the values of software data sets\u27 attributes and study the impact of this change on the modules\u27 defect status. The goal is to find those specific attributes that highly correlate with the module defect attribute. An algorithm is developed to automatically predict the module defect status based on the values of the module attributes and based on their change from reference or initial values. For each attribute of those software projects, results can show when such attribute can be, if any, a major player in deciding the defect status of the project or a specific module. Results showed consistent, and in some cases better, results in comparison with most surveyed defect prediction algorithms. Results showed also that this can be a very powerful method to understand each attribute individual impact, if any, to the module quality status and how it can be improved

Preparation of activated carbon from tamarind seeds and Methylene blue (MB) removal

In this study, thedye treatment which is methylene blue (MB)  as water pollutants was ascertained with the activated carbon that prepared from the tamarind seed(Tamarindusindica). The conditions used to prepare activated carbon, (TSC) were activated using phosphoric acid as an activating agent at temperature 500 oC for four hours. These conditions may be attributed to enhance the surface area and pores development of TSC. Single point BET surface area (SBET) analysis gave the surface area of TSC was and FESEM analysis showed that the pores development and formation were mostly in circle and oval pattern. The best conditions for TSC sample to adsorb MB effectively were at 50 mL of MB with concentration 120 ppm at temperature, 323 K by using 0.04 g of TSC. The maximum adsorption capacity for MB dye solution was 102.77 mg g-1Keywords: Tamarind seed; dyes; methylene blue; adsorption; phosphoric acid