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Given that computational thinking (CT) is still a blurry psychological construct, its assessment remains as a thorny, unresolved issue. Hence, in recent years, several assessment tools have been developed from different approaches and operational definitions of CT. However, very little research has been conducted to study whether these instruments provide convergent measurements, and how to combine them properly in educational settings. In response, we first review a myriad of CT assessment tools and classify them according to their evaluative approach. Second, we report the results of two convergent validity studies that involve three of these CT assessment tools, which come from different perspectives: the Computational Thinking Test, the Bebras Tasks, and Dr. Scratch. Finally, we propose a comprehensive model to evaluate the development of CT within educational scenarios and interventions, which includes the aforementioned and other reviewed assessment tools. Our comprehensive model intends to assess CT along every cognitive level of Bloom's taxonomy and throughout the various stages of typical educational interventions. Furthermore, the model explicitly indicates how to harmoniously combine the different types of CT assessment tools in order to give answer to the most common research questions in the field of CT Education. Thus, this contribution may lead scholars and policy-makers to perform accurate evaluation designs of CT according to their inquiry goals.

Context As a novel coronavirus swept the world in early 2020, thousands of software developers began working from home. Many did so on short notice, under difficult and stressful conditions. Objective This study investigates the effects of the pandemic on developers’ wellbeing and productivity. Method A questionnaire survey was created mainly from existing, validated scales and translated into 12 languages. The data was analyzed using non-parametric inferential statistics and structural equation modeling. Results The questionnaire received 2225 usable responses from 53 countries. Factor analysis supported the validity of the scales and the structural model achieved a good fit (CFI = 0.961, RMSEA = 0.051, SRMR = 0.067). Confirmatory results include: (1) the pandemic has had a negative effect on developers’ wellbeing and productivity; (2) productivity and wellbeing are closely related; (3) disaster preparedness, fear related to the pandemic and home office ergonomics all affect wellbeing or productivity. Exploratory analysis suggests that: (1) women, parents and people with disabilities may be disproportionately affected; (2) different people need different kinds of support. Conclusions To improve employee productivity, software companies should focus on maximizing employee wellbeing and improving the ergonomics of employees’ home offices. Women, parents and disabled persons may require extra support.

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The huge quantities of data available in the CVS repositories of large, long-lived libre (free, open source) software projects, and the many interrelationships among those data offer opportunities for extracting large amounts of valuable information about their structure, evolution and internal processes. Unfortunately, the sheer volume of that information renders it almost unusable without applying methodologies which highlight the relevant information for a given aspect of the project. In this paper, we propose the use of a well known set of methodologies (social network analysis) for characterizing libre software projects, their evolution over time and their internal structure. In addition, we show how we have applied such methodologies to real cases, and extract some preliminary conclusions from that experience.

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Abstract-ThisWe have analyzed the papers that contained any experimental analysis of software projects for their potentiality of being replicated. In this regard, three main issues have been addressed: i) the public availability of the data used as case study, ii) the public availability of the processed dataset used by researchers and iii) the public availability of the tools and scripts. A total number of 171 papers have been analyzed from the six workshops/working conferences up to date. Results show that MSR authors use in general publicly available data sources, mainly from free software repositories, but that the amount of publicly available processed datasets is very low. Regarding tools and scripts, for a majority of papers we have not been able to find any tool, even for papers where the authors explicitly state that they have built one. Lessons learned from the experience of reviewing the whole MSR literature and some potential solutions to lower the barriers of replicability are finally presented and discussed.