Social origins, geographical mobility and occupational attainment in contemporary Italy

AbstractThis paper studies the effect in the Italian case of geographical mobility on employment and occupational attainment, defined as access to the upper class, avoidance of the working class, and avoidance of agricultural jobs. It observes the distribution of its effect over the life course. Given that migration is a gendered phenomenon, we perform separate analyses by gender. Our data set, moreover, includes residential information at the municipality level, making it possible to specify geographical mobility in different ways, according to the distance, the characteristics of origin, and destination and the frequency of individual movements. Third, it studies whether the effects of geographical mobility change according to social class of origin and geographical area of origin.Our analyses, based on linear probability panel models with fixed effects, show a strong gender divide concerning the probability of employment and avoidance of the working class. A positive effect of geographical mobility on occupational outcomes appears to exist only as regards men, because for women the divergence between movers and stayers appears well before geographical mobility. Finally, the effects of geographical mobility are generally stronger for individuals originating from the middle and lower classes and from rural areas, but they are not so strong as to enable those individuals to substantially change their position in the occupational hierarchy

DeepTC-Enhancer: Improving the Readability of Automatically Generated Tests

Automated test case generation tools have been successfully proposed to reduce the amount of human and infrastructure resources required to write and run test cases. However, recent studies demonstrate that the readability of generated tests is very limited due to (i) uninformative identifiers and (ii) lack of proper documentation. Prior studies proposed techniques to improve test readability by either generating natural language summaries or meaningful methods names. While these approaches are shown to improve test readability, they are also affected by two limitations: (1) generated summaries are often perceived as too verbose and redundant by developers, and (2) readable tests require both proper method names but also meaningful identifiers (within-method readability). In this work, we combine template based methods and Deep Learning (DL) approaches to automatically generate test case scenarios (elicited from natural language patterns of test case statements) as well as to train DL models on path-based representations of source code to generate meaningful identifier names. Our approach, called DeepTC-Enhancer, recommends documentation and identifier names with the ultimate goal of enhancing readability of automatically generated test cases. An empirical evaluation with 36 external and internal developers shows that (1) DeepTC-Enhancer outperforms significantly the baseline approach for generating summaries and performs equally with the baseline approach for test case renaming, (2) the transformation proposed by DeepTC-Enhancer results in a significant increase in readability of automatically generated test cases, and (3) there is a significant difference in the feature preferences between external and internal developers.Accepted author manuscriptSoftware Engineerin