Abstract Proper software modularization still poses challenges to developers. One of the symptoms of inappropriate modularization is the large size of object‐oriented classes. In that case, a possible solution would be class restructuring with refactorings, such asExtract Class,Extract Super‐Class, orMove Method. However, class refactoring is challenging because of the possible side effects of improper changes. In this context, more effective decision support systems on which classes are worthwhile for restructuring to improve modularity are still lacking. This work focuses on exploring possible alternatives for supporting decision on class restructuring. A prospective study was performed on selected kinds of restructuring, aiming at determining what types of strategies are typically adopted to restructure bloated classes and which classes developers decided to restructure. Then, we proposed and evaluated a predictive model for indicating which classes to restructure, aiming at delivering a restructuring guide on those classes. Finally, we conducted a qualitative study to evaluate the perception of developers on such guides based on predictions for real software. The results have shown situations in which the proposed predictions could help the restructuring process but also elucidated possible improvements and limitations.

Bad smells are sub-optimal code structures that may represent problems needing attention. We conduct an extensive literature review on bad smells relying on a large body of knowledge from 1990 to 2017. We show that some smells are much more studied in the literature than others, and also that some of them are intrinsically inter-related (which). We give a perspective on how the research has been driven across time (when). In particular, while the interest in duplicated code emerged before the reference publications by Fowler and Beck and by Brown et al., other types of bad smells only started to be studied after these seminal publications, with an increasing trend in the last decade. We analyzed aims, findings, and respective experimental settings, and observed that the variability of these elements may be responsible for some apparently contradictory findings on bad smells (what). Moreover, we could observe that, in general, papers tend to study different types of smells at once. However, only a small percentage of those papers actually investigate possible relations between the respective smells (co-studies), i.e., each smell tends to be studied in isolation. Despite of a few relations between some types of bad smells have been investigated, there are other possible relations for further investigation. We also report that authors have different levels of interest in the subject, some of them publishing sporadically and others continuously (who). We observed that scientific connections are ruled by a large “small world” connected graph among researchers and several small disconnected graphs. We also found that the communities studying duplicated code and other types of bad smells are largely separated. Finally, we observed that some venues are more likely to disseminate knowledge on Duplicate Code (which often is listed as a conference topic on its own), while others have a more balanced distribution among other smells (where). Finally, we provide a discussion on future directions for bad smell research.

The high use of system calls in many applications has motivated research works focusing on reducing the overhead caused by these calls. In this work we implement three types of compound system calls, which are evaluated experimentally by taking into account their execution time in systems with 1 to 4 cores. All implemented system calls present performance gains when compared to their conventional counterparts. The data obtained through controlled experiments are analyzed performing ANOVA and Tukey tests. The best results show reductions in the execution time between 50 to 60 percent.