A large-scale empirical exploration on refactoring activities in open source software projects

Refactoring is a well-established practice that aims at improving the internal structure of a software system without changing its external behavior. Existing literature provides evidence of how and why developers perform refactoring in practice. In this paper, we continue on this line of research by performing a large-scale empirical analysis of refactoring practices in 200 open source systems. Specifically, we analyze the change history of these systems at commit level to investigate: (i) whether developers perform refactoring operations and, if so, which are more diffused and (ii) when refactoring operations are applied, and (iii) which are the main developer-oriented factors leading to refactoring. Based on our results, future research can focus on enabling automatic support for less frequent refactorings and on recommending refactorings based on the developer's workload, project's maturity and developer's commitment to the project

First record of Zamenis lineatus (Camerano, 1891) (Serpentes Colubridae) in Ischia Island (Italy)

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Theatre is a valid add-on therapeutic intervention for emotional rehabilitation of parkinson's disease patients

Conventional medical treatments of Parkinson's disease (PD) are effective on motor disturbances but may have little impact on nonmotor symptoms, especially psychiatric ones. Thus, even when motor symptomatology improves, patients might experience deterioration in their quality of life. We have shown that 3 years of active theatre is a valid complementary intervention for PD as it significantly improves the well-being of patients in comparison to patients undergoing conventional physiotherapy. Our aim was to replicate these findings while improving the efficacy of the treatment. We ran a single-blinded pilot study lasting 15 months on 24 subjects with moderate idiopathic PD. 12 were assigned to a theatre program in which patients underwent "emotional" training. The other 12 underwent group physiotherapy. Patients were evaluated at the beginning and at the end of their treatments, using a battery of eight clinical and five neuropsychological scales. We found that the emotional theatre training improved the emotional well-being of patients, whereas physiotherapy did not. Interestingly, neither of the groups showed improvements in either motor symptoms or cognitive abilities tested by the neuropsychological battery. We confirmed that theatre therapy might be helpful in improving emotional well-being in PD

Branch coverage prediction in automated testing

This is the peer reviewed version which has been published in final form at [DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Software testing is crucial in continuous integration (CI). Ideally, at every commit, all the test cases should be executed, and moreover, new test cases should be generated for the new source code. This is especially true in a Continuous Test Generation (CTG) environment, where the automatic generation of test cases is integrated into the continuous integration pipeline. In this context, developers want to achieve a certain minimum level of coverage for every software build. However, executing all the test cases and, moreover, generating new ones for all the classes at every commit is not feasible. As a consequence, developers have to select which subset of classes has to be tested and/or targeted by test‐case generation. We argue that knowing a priori the branch coverage that can be achieved with test‐data generation tools can help developers into taking informed decision about those issues. In this paper, we investigate the possibility to use source‐code metrics to predict the coverage achieved by test‐data generation tools. We use four different categories of source‐code features and assess the prediction on a large data set involving more than 3'000 Java classes. We compare different machine learning algorithms and conduct a fine‐grained feature analysis aimed at investigating the factors that most impact the prediction accuracy. Moreover, we extend our investigation to four different search budgets. Our evaluation shows that the best model achieves an average 0.15 and 0.21 MAE on nested cross‐validation over the different budgets, respectively, on EVOSUITE and RANDOOP. Finally, the discussion of the results demonstrate the relevance of coupling‐related features for the prediction accuracy

Data-Driven Decisions and Actions in Today’s Software Development

Today’s software development is all about data: data about the software product itself, about the process and its different stages, about the customers and markets, about the development, the testing, the integration, the deployment, or the runtime aspects in the cloud. We use static and dynamic data of various kinds and quantities to analyze market feedback, feature impact, code quality, architectural design alternatives, or effects of performance optimizations. Development environments are no longer limited to IDEs in a desktop application or the like but span the Internet using live programming environments such as Cloud9 or large-volume repositories such as BitBucket, GitHub, GitLab, or StackOverflow. Software development has become “live” in the cloud, be it the coding, the testing, or the experimentation with different product options on the Internet. The inherent complexity puts a further burden on developers, since they need to stay alert when constantly switching between tasks in different phases. Research has been analyzing the development process, its data and stakeholders, for decades and is working on various tools that can help developers in their daily tasks to improve the quality of their work and their productivity. In this chapter, we critically reflect on the challenges faced by developers in a typical release cycle, identify inherent problems of the individual phases, and present the current state of the research that can help overcome these issues

Effects of recombinant Irisin on the musculoskeletal system of hind-limb suspended mice

We previously showed that Irisin, a myokine released from skeletal muscle after physical exercise, plays a central role in the control of bone mass, driving positive effects on cortical mineral density and geometry in vivo (1). Here we demonstrated that r-Irisin treatment prevents bone loss in hind-limb suspended mice when administered during suspension and recovers bone mass when mice were injected after a suspension period (4 weeks) during which they developed bone loss. Micro computed tomography of femurs showed that r-Irisin treatment positively affected both cortical and trabecular bone. As expected, unloaded mice treated with vehicle displayed a remarkable decrease of cortical and trabecular bone mineral density (BMD), whereas in Irisin-treated unloaded mice no loss of BMD was observed with respect to control mice kept under normal loading. Likewise, by treating mice after they already developed disuse-induced bone loss, r-Irisin was able to restore the damaged mineral component. Furthermore, trabecular bone volume fraction (BV/TV), which dramatically decreased in unloaded mice, was prevented by r-Irisin therapy. In particular, r-Irisin treatment preserved the number of trabeculae (Tb.N) and the fractal dimension, an index of optimal micro-architectural complexity of trabecular bone.We also showed that r-Irisin treatment protects muscle mass suffering from atrophy during unloading. Thus, unloaded mice treated with vehicle displayed a severe loss of muscle mass, as confirmed by ~ 60% decline of vastus lateralis weight and ~33% decrease of fiber cross-sectional area. Conversely, Irisin-treated unloaded mice showed no loss of muscle weight and similar fiber cross-sectional area to control mice. Our data reveal for the first time that r-Irisin treatment prevents and retrieves disuse-induced bone loss and muscle atrophy. These findings may lead to develop an Irisin-based therapy for the prevention and treatment of osteoporosis and sarcopenia in all patients who cannot perform physical activity, as occurs during aging and immobility, and it could also represent a countermeasure for astronauts exposed to microgravity during space flight missions.This work was supported in part by ERISTO grant (to M.G.), by MIUR grant ex60% (to M.G.) and by SIOMMMS grant (to G.C.)

The psychological science accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

In COVID-19 Health Messaging, Loss Framing Increases Anxiety with Little-to-No Concomitant Benefits: Experimental Evidence from 84 Countries

The COVID-19 pandemic (and its aftermath) highlights a critical need to communicate health information effectively to the global public. Given that subtle differences in information framing can have meaningful effects on behavior, behavioral science research highlights a pressing question: Is it more effective to frame COVID-19 health messages in terms of potential losses (e.g., "If you do not practice these steps, you can endanger yourself and others") or potential gains (e.g., "If you practice these steps, you can protect yourself and others")? Collecting data in 48 languages from 15,929 participants in 84 countries, we experimentally tested the effects of message framing on COVID-19-related judgments, intentions, and feelings. Loss- (vs. gain-) framed messages increased self-reported anxiety among participants cross-nationally with little-to-no impact on policy attitudes, behavioral intentions, or information seeking relevant to pandemic risks. These results were consistent across 84 countries, three variations of the message framing wording, and 560 data processing and analytic choices. Thus, results provide an empirical answer to a global communication question and highlight the emotional toll of loss-framed messages. Critically, this work demonstrates the importance of considering unintended affective consequences when evaluating nudge-style interventions