Analysis of Snow Cover in the Sibillini Mountains in Central Italy

Research on solid precipitation and snow cover, especially in mountainous areas, suffers from problems related to the lack of on-site observations and the low reliability of measurements, which is often due to instruments that are not suitable for the environmental conditions. In this context, the study area is the Monti Sibillini National Park, and it is no exception, as it is a mountainous area located in central Italy, where the measurements are scarce and fragmented. The purpose of this research is to provide a characterization of the snow cover with regard to maximum annual snow depth, average snow depth during the snowy period, and days with snow cover on the ground in the Monti Sibillini National Park area, by means of ground weather stations, and also analyzing any trends over the last 30 years. For this research, in order to obtain reliable snow cover data, only data from weather stations equipped with a sonar system and manual weather stations, where the surveyor goes to the site each morning and checks the thickness of the snowpack and records, it were collected. The data were collected from 1 November to 30 April each year for 30 years, from 1991 to 2020; six weather stations were taken into account, while four more were added as of 1 January 2010. The longer period was used to assess possible ongoing trends, which proved to be very heterogeneous in the results, predominantly negative in the case of days with snow cover on the ground, while trends were predominantly positive for maximum annual snow depth and distributed between positive and negative for the average annual snow depth. The shorter period, 2010–2022, on the other hand, ensured the presence of a larger number of weather stations and was used to assess the correlation and presence of clusters between the various weather stations and, consequently, in the study area. Furthermore, in this way, an up-to-date nivometric classification of the study area was obtained (in terms of days with snow on the ground, maximum height of snowpack, and average height of snowpack), filling a gap where there had been no nivometric study in the aforementioned area. The interpolations were processed using geostatistical techniques such as co-kriging with altitude as an independent variable, allowing fairly precise spatialization, analyzing the results of cross-validation. This analysis could be a useful tool for hydrological modeling of the area, as well as having a clear use related to tourism and vegetation, which is extremely influenced by the nivometric variables in its phenology. In addition, this analysis could also be considered a starting point for the calibration of more recent satellite products dedicated to snow cover detection, in order to further improve the compiled climate characterizatio

Action-based recommendation in pull-request development

Pull requests (PRs) selection is a challenging task faced by integrators in pull-based development (PbD), with hundreds of PRs submitted on a daily basis to large open-source projects. Managing these PRs manually consumes integrators' time and resources and may lead to delays in the acceptance, response, or rejection of PRs that can propose bug fixes or feature enhancements. On the one hand, well-known platforms for performing PbD, like GitHub, do not provide built-in recommendation mechanisms for facilitating the management of PRs. On the other hand, prior research on PRs recommendation has focused on the likelihood of either a PR being accepted or receive a response by the integrator. In this paper, we consider both those likelihoods, this to help integrators in the PRs selection process by suggesting to them the appropriate actions to undertake on each specific PR. To this aim, we propose an approach, called CARTESIAN (aCceptance And Response classificaTion-based requESt IdentificAtioN) modeling the PRs recommendation according to PR actions. In particular, CARTESIAN is able to recommend three types of PR actions: accept, respond, and reject. We evaluated CARTESIAN on the PRs of 19 popular GitHub projects. The results of our study demonstrate that our approach can identify PR actions with an average precision and recall of about 86%. Moreover, our findings also highlight that CARTESIAN outperforms the results of two baseline approaches in the task of PRs selection

Analyzing APIs documentation and code to detect directive defects

Application Programming Interface (API) documents represent one of the most important references for API users. However, it is frequently reported that the documentation is inconsistent with the source code and deviates from the API itself. Such inconsistencies in the documents inevitably confuse the API users hampering considerably their API comprehension and the quality of software built from such APIs. In this paper, we propose an automated approach to detect defects of API documents by leveraging techniques from program comprehension and natural language processing. Particularly, we focus on the directives of the API documents which are related to parameter constraints and exception throwing declarations. A first-order logic based constraint solver is employed to detect such defects based on the obtained analysis results. We evaluate our approach on parts of well documented JDK 1.8 APIs. Experiment results show that, out of around 2000 API usage constraints, our approach can detect 1158 defective document directives, with a precision rate of 81.6%, and a recall rate of 82.0%, which demonstrates its practical feasibility

Exploring assembly trajectories of abandoned grasslands in response to 10 years of mowing in sub-mediterranean context

Abandoned semi-natural grasslands are characterized by lower plant diversity as a consequence of tall grasses spreading. Mowing is a widely used restoration practice, but its effects to maintain the restored diversity over time is poorly investigated in sub-Mediterranean grasslands. Since 2010 in the central Apennines, we fenced a grassland, invaded by Brachypodium rupestre, which was mowed twice a year. Before the experiment started, we recorded species cover in 30 random sampling units (0.5 m × 0.5 m). The sampling was repeated every two years for a total of ten years. We used linear mixed-effect models to investigate the trajectory of functional diversity and community weighted mean for traits related to space occupation, resource exploitation, temporal niche exploitation, and Grime’s CSR strategies. The reduction of the weaker competitor exclusion exerted by B. rupestre affected the functional plant community. In the short term (4-6 years), this fostered space occupation strategies, decreasing convergence of clonal strategies and horizontal space occupation types. In the longer term (8-10 years), mowing filtered ruderal strategies, i.e., species with faster resource acquisition (lower leaf dry matter content, LDMC). LDMC and CSR strategies, initially convergent due to the dominance of B. rupestre, lowered convergence over time due to higher differentiation of strategies

Building an Ensemble for Software Defect Prediction Based on Diversity Selection

Background: Ensemble techniques have gained attention in various scientific fields. Defect prediction researchers have investigated many state-of-the-art ensemble models and concluded that in many cases these outperform standard single classifier techniques. Almost all previous work using ensemble techniques in defect prediction rely on the majority voting scheme for combining prediction outputs, and on the implicit diversity among single classifiers. Aim: Investigate whether defect prediction can be improved using an explicit diversity technique with stacking ensemble, given the fact that different classifiers identify different sets of defects. Method: We used classifiers from four different families and the weighted accuracy diversity (WAD) technique to exploit diversity amongst classifiers. To combine individual predictions, we used the stacking ensemble technique. We used state-of-the-art knowledge in software defect prediction to build our ensemble models, and tested their prediction abilities against 8 publicly available data sets. Conclusion: The results show performance improvement using stacking ensembles compared to other defect prediction models. Diversity amongst classifiers used for building ensembles is essential to achieving these performance improvements

Commonality-Driven Unit Test Generation

Various search-based test generation techniques have been proposed to automate the generation of unit tests fulfilling different criteria (e.g., line coverage, branch coverage, mutation score, etc.). Despite several advances made over the years, search-based unit test generation still suffers from a lack of guidance due to the limited amount of information available in the source code that, for instance, hampers the generation of complex objects. Previous studies introduced many strategies to address this issue, e.g., dynamic symbolic execution or seeding, but do not take the internal execution of the methods into account. In this paper, we introduce a novel secondary objective called commonality score, measuring how close the execution path of a test case is from reproducing a common or uncommon execution pattern observed during the operation of the software. To assess the commonality score, we implemented it in EvoSuite and evaluated its application on 150 classes from JabRef, an open-source software for managing bibliography references. Our results are mixed. Our approach leads to test cases that indeed follow common or uncommon execution patterns. However, if the commonality score can have a positive impact on the structural coverage and mutation score of the generated test suites, it can also be detrimental in some cases.Software Engineerin