Daniel Rodriguez

TL;DR: This study concludes that in order to apply statistical or data mining techniques to these type of repositories extensive preprocessing of the data needs to be performed due to ambiguities, wrongly recorded values, missing values, unbalanced datasets, etc.

TL;DR: This paper compares different approaches, namely sampling, cost-sensitive, ensemble and hybrid approaches to the problem of defect prediction with different datasets preprocessed differently, using the well-known NASA datasets curated by Shepperd et al.

TL;DR: This paper makes use of attribute selection techniques in different datasets publicly available (PROMISE repository), and different data mining algorithms for classification to defect faulty modules, and shows that in general, smaller datasets with less attributes maintain or improve the prediction capability with less Attributes than the original datasets.

TL;DR: How and when the laws of software evolution evolved and how they are perceived by the research community are described, and the developments and challenges that are likely to occur in the coming years are addressed.

TL;DR: A novel system which is capable of adapting to the user's thermal preferences without any prior knowledge, and measuring his comfort level by aggregating several thermal parameters into one single thermal index is proposed.