Variable Names Research Articles

Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability

Abstract. The widely used open-source community Noah with multi-parameterization options (Noah-MP) land surface model (LSM) is designed for applications ranging from uncoupled land surface hydrometeorological and ecohydrological process studies to coupled numerical weather prediction and decadal global or regional climate simulations. It has been used in many coupled community weather, climate, and hydrology models. In this study, we modernize and refactor the Noah-MP LSM by adopting modern Fortran code standards and data structures, which substantially enhance the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization as a result of re-organizing model physics into individual process-level Fortran module files, (2) an enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) an enhanced code structure and calling workflow as a result of leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standards, and (5) enhanced driver and interface structures to be coupled with the host weather, climate, and hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather, climate, and hydrology models in the future. Overall, the modernized Noah-MP allows a more efficient and convenient process for future model developments and applications.

Preprocessing of Propagation Redundant Clauses

The propagation redundant (PR) proof system generalizes the resolution and resolution asymmetric tautology proof systems used by conflict-driven clause learning (CDCL) solvers. PR allows short proofs of unsatisfiability for some problems that are difficult for CDCL solvers. Previous attempts to automate PR clause learning used hand-crafted heuristics that work well on some highly-structured problems. For example, the solver SaDiCaL incorporates PR clause learning into the CDCL loop, but it cannot compete with modern CDCL solvers due to its fragile heuristics. We present PReLearn, a preprocessing technique that learns short PR clauses. Adding these clauses to a formula reduces the search space that the solver must explore. By performing PR clause learning as a preprocessing stage, PR clauses can be found efficiently without sacrificing the robustness of modern CDCL solvers. On a large portion of SAT competition benchmarks we found that preprocessing with PReLearn improves solver performance. In addition, there were several satisfiable and unsatisfiable formulas that could only be solved after preprocessing with PReLearn. PReLearn supports proof logging, giving a high level of confidence in the results. Lastly, we tested the robustness of PReLearn by applying other forms of preprocessing as well as by randomly permuting variable names in the formula before running PReLearn, and we found PReLearn performed similarly with and without the changes to the formula.

An automated detection of confusing variable pairs with highly similar compound names in Java and Python programs

Variable names represent a significant source of information regarding the source code, and a successful naming of variables is key to producing readable code. Programmers often use a compound variable name by concatenating two or more words to make it more informative and enhance the code readability. While each compound variable name is descriptive, a collection of them sometimes produces “confusing” variable pairs if their names are highly similar, e.g., “shippingHeight,” vs. “shippingWeight.” A confusing variable pair would adversely affect the code readability because it can cause a misreading or mix-up of variables during the programming or code review activities. Toward automated support for enhancing code readability, this paper conducts a large-scale investigation of compound variable names in Java and Python programs. The investigation collects 116,921,127 pairs of compound-named variables from 1,876 open-source Java projects and 106,943,523 pairs of such variables from 2,427 open-source Python projects. Then, this study analyzes those variable pairs from two perspectives of name similarity: string similarity and semantic similarity. Through an evaluation study with 30 human participants, the data analyses show that both string and semantic similarity can help detect confusing variable pairs in Java and Python programs. In order to distill confusing variable pairs automatically, support tools for detecting confusing variable pairs are also developed in this study.

Using the uniqueness of global identifiers to determine the provenance of Python software source code

We consider the problem of identifying the provenance of free/open source software (FOSS) and specifically the need of identifying where reused source code has been copied from. We propose a lightweight approach to solve the problem based on software identifiers-such as the names of variables, classes, and functions chosen by programmers. The proposed approach is able to efficiently narrow down to a small set of candidate origin products, to be further analyzed with more expensive techniques to make a final provenance determination.By analyzing the PyPI (Python Packaging Index) open source ecosystem we find that globally defined identifiers are very distinct. Across PyPI's 244 K packages we found 11.2 M different global identifiers (classes and method/function names-with only 0.6% of identifiers shared among the two types of entities); 76% of identifiers were used only in one package, and 93% in at most 3. Randomly selecting 3 non-frequent global identifiers from an input product is enough to narrow down its origins to a maximum of 3 products within 89% of the cases.We validate the proposed approach by mapping Debian source packages implemented in Python to the corresponding PyPI packages; this approach uses at most five trials, where each trial uses three randomly chosen global identifiers from a randomly chosen python file of the subject software package, then ranks results using a popularity index and requires to inspect only the top result. In our experiments, this method is effective at finding the true origin of a project with a recall of 0.9 and precision of 0.77.

The variable stars in the field of NGC 6139: a critical approach to their reddening and membership

ABSTRACT We present a CCD VI time-series analysis of the globular cluster NGC 6139 and its variable star population. Using the astrometric data available in Gaia-DR3, we performed a membership analysis that enabled the construction of a clean colour–magnitude diagram (CMD). Variable stars in the field of the cluster reported by Gaia-DR3 and newly discovered ones in this paper are classified and their membership is critically evaluated. We report two cluster members RRc (V12, V15) and four SR (V13, V14, V17, V18) not previously detected and assign variable names to V11 and V16 detected by Gaia as they proof to be cluster members. Light curves and periods for non-member Gaia eclipsing binaries, semiregular variables, and newly detected RR Lyrae stars are provided. Fourier decomposition of the light curves of the cluster member RRab and RRc stars leads to the values [Fe/H] = −1.63 dex, and distance of 9.63 ± 0.68 kpc. The Oosterhoff type II nature of the cluster is confirmed. We adopted the mean reddening E(B − V) = 0.786 mag and performed a differential reddening analysis based on the dispersion of the red giant branch. The differential map allowed a mild correction of the CMD.

Management of Indirect Hyperbilirubinemia; Comparison of Complications of Light Emitting Diode (LED) and Fluorescent Phototherapy

Objective: To compare the frequency of complications (hyperthermia, skin eruption, dehydration) of LED phototherapy and fluorescent phototherapy in management of indirect hyperbilirubinemia in neonates Material and methods: This randomized control study was carried out from 1st June 2015 to 31 December 2016 at NNU of The Children`s Hospital Lahore using Non probability consecutive sampling technique after getting informed consent from parents. All basic demographic information of each variable (name, age) was noted and entered on Performa. SPSS-18 was used to interpret the data. Results: In our study the mean age was 1.98±0.83 days. Skin rash complication was observed in 43(13%) cases, dehydration was found in 48(14.5%) cases and hyperthermia was noted in 65(19.70%) patients. Practical Implication: Many setups in Pakistan are still using conventional fluorescent phototherapy machines. LED has lesser side effects as compared to conventional one so by using LED light source we can lessen the adverse effects of phototherapy that will be beneficial to the babies Conclusion: LED light source is effective for phototherapy with significantly lesser complication rate (hyperthermia, skin eruption, and dehydration) compared to fluorescent phototherapy in management of indirect hyperbilirubinemia in neonates. In this study LED phototherapy significantly showed fewer complications as compared to fluorescent phototherapy cases. Keywords: LED, Fluorescent, Hyperthermia, Skin, Rash, Dehydration, Neonates, Hyperbilirubinemia

CDISC Enables Efficient Streamlining of Clinical Trial Safety Evaluation

The use of standardized data formats has increasingly facilitated both the submission of clinical trial data by pharmaceutical companies and its review by regulatory agencies. The Clinical Data Interchange Standards Consortium (CDISC) format has become the de facto worldwide standard for clinical trial data submissions and review. CDISC data are captured and organized in defined datasets, or domains, with predefined variable names and attributes. As a result, researchers and reviewers have a consistent, repeatable, and rapid way to load, review, and analyze data. The benefits provided by this standardization have culminated in the development of software that intuitively utilizes CDISC structures to quickly import and analyze the data as well as summarize the results for scientists and clinicians in a collection of readable and easy-to-understand reports. In this paper, we will show how CDISC datasets with their required domains and respective variables can be utilized by JMP® Clinical to create clinical trial analysis reports easily and quickly with interactive graphs and tables for use in FDA New Drug Applications and Clinical Reviews. These reports are also ready for inclusion in regulatory submissions such as regulatory documents and clinical study reports to improve reviewability, interpretability, and efficiency of regulatory submission and review.

International Comparison of Ambulance Times Terminology and Definitions: A Benchmarking Study

Introduction:Ambulance times are internationally recognized Key Performance Indicators (KPI) for prehospital care. International benchmarking by comparing ambulance times between countries is a valuable method to help to identify strengths and weaknesses across healthcare systems. However, ambulance times are not standardized across or sometimes even within countries. Thus, this benchmarking study aims to compare terminology and definitions of ambulance times from the ambulance services of a range of countries to facilitate international benchmarking.Method:A 23-point questionnaire was developed and pilot-tested on members of international emergency care organizations. The final questionnaire was administered to domestic and international Ambulance Services, who use the Advanced Medical Priority Dispatch System, asking for the terminology and definitions for times from “call received” to “arrival at hospital”. This included “clock start” and “clock stop” times. We asked for the ambulance terms and related variable names in the computer aided dispatch/reporting system. We engaged with clinical stakeholders and Patient and Public Involvement Contributors throughout the process.Results:We gathered information from 10 international ambulance services, representing nine countries, and three continents. Some services in the United Kingdom have standardized ambulance times terminology and definitions. However, in the majority of cases terminology differed greatly between countries, and at times within countries and between reports. Definitions of ambulance times varied between countries and regions, with some having different clock start and stop times and others not collecting data on the same time periods.Conclusion:The current level of variation in international ambulance times terminology and definitions poses a challenge for international benchmarking and research. International consensus or harmonization of language and definitions would result in more efficient and accurate global comparison. On a smaller scale, defining terms in publications and reports would begin facilitating this process.

Idealized hydrodynamical numerical model dataset with no-river runoff at the western tropical North Atlantic.

The western tropical North Atlantic (WTNA) is a very complex region, with the influence of intense western boundary currents in connection with equatorial zonal currents, important atmospheric forcings (e.g Intertropical Convergence Zone), mesoscale activities (e.g NBC rings), and the world's largest river discharge as the Amazon River runoff. The volume discharge is equivalent to more than one-third of the Atlantic river freshwater input, with a plume that spreads over the region reaching the northwestward Caribbean Sea and eastward longitudes of 30°W, andinfluencing from physical to biological structures. Therefore, in order to enable and encourage more understanding of the region, here we present a dataset based on an idealized scenario of no river runoff of the Amazon River and Par ´a River in the WTNA. The numerical simulations were conducted with a regional oceanicmodeling system (ROMS) model and three pairs of files were generated with themodel outputs: (i) ROMS-files, with the parameters of the ROMS-outputs raw data in a NetCDF format and monthly and weekly frequencies; (ii) MATLAB-files, which contain oceanographic parameters also in monthly and weekly frequencies; and (iii) NetCDF-files, with oceanographic parameters again in monthly and weekly frequencies. For each file, we present the coordinates and variable names, descriptions, and correspondent units. The dataset is available in the Science Data Bank repository (doi: https://doi.org/10.57760/sciencedb.02145).

A versatile workflow for linear modelling in R

Linear models are applied widely to analyse empirical data. Modern software allows implementation of linear models with a few clicks or lines of code. While convenient, this increases the risk of ignoring essential assessment steps. Indeed, inappropriate application of linear models is an important source of inaccurate statistical inference. Despite extensive guidance and detailed demonstration of exemplary analyses, many users struggle to implement and assess their own models. To fill this gap, we present a versatile R-workflow template that facilitates (Generalized) Linear (Mixed) Model analyses. The script guides users from data exploration through model formulation, assessment and refinement to the graphical and numerical presentation of results. The workflow accommodates a variety of data types, distribution families, and dependency structures that arise from hierarchical sampling. To apply the routine, minimal coding skills are required for data preparation, naming of variables of interest, linear model formulation, and settings for summary graphs. Beyond that, default functions are provided for visual data exploration and model assessment. Focused on graphs, model assessment offers qualitative feedback and guidance on model refinement, pointing to more detailed or advanced literature where appropriate. With this workflow, we hope to contribute to research transparency, comparability, and reproducibility.

DDOFM: Dynamic malicious domain detection method based on feature mining

The domain name system is an essential part of the network, and target hosts are often attacked by malicious domain names to steal resources. Some traditional detection methods have low accuracy, poor generalization ability, and high resource overhead on model construction to deal with complex and variable malicious domain names. A three-level dynamic malicious domain detection method (DDOFM) is proposed in this paper. DDOFM only needs to combine a few high-order statistical features of benign domains with some DNS features, without flagging malicious samples and involving them in training. Firstly, the boundary recognition of passive DNS (PDNS) features extracted from DNS traffic is carried out to conduct an early- warning for some domains. Second, the Hidden Markov Model (HMM) forward algorithm and normal distribution probability density function are used to calculate the formation probabilities of the warned domains and their probability density values. Then the probabilities of every character in the warned domain name and the standard deviation between these probabilities are counted. Further, the probability density values and the standard deviations of these warned domain names are compared with their respective thresholds to identify the attribution of the warned domain names. Finally, if the domain name is not warned, the Jensen–Shannon divergence (JS divergence) between it and the previous domain name will be calculated. Then the local iterative threshold finding algorithm (LLTFA) proposed in this paper will be combined to determine its attribution and identify whether the host is connected to the command and control (C&C) server. Experiments show that the detection indexes of this method exceed 99% for multiple types of malicious domain names. The C&C servers can also be identified by DDOFM faster than similar methods.

How to Use Simplified Reproductive Calendar Data from the Demographic and Health Survey.

IPUMS Demographic and Health Surveys (IPUMS DHS), through its intuitive website (http://dhs.ipums.org/), eliminate barriers to overtime and cross-national analyses with the DHS. IPUMS DHS recently released simplified reproductive calendar data. These calendar data are harmonized across samples, distinguish "not in universe" cases from "no" responses, and do not require destringing. Variable names are hot links to important documentation, such as survey-question text and comparability concerns. Analysts can also select consistently coded variables relating to the woman, her household, and her social and environmental context without merging files.

DIRE and its Data: Neural Decompiled Variable Renamings with Respect to Software Class

The decompiler is one of the most common tools for examining executable binaries without the corresponding source code. It transforms binaries into high-level code, reversing the compilation process. Unfortunately, decompiler output is far from readable because the decompilation process is often incomplete. State-of-the-art techniques use machine learning to predict missing information like variable names. While these approaches are often able to suggest good variable names in context, no existing work examines how the selection of training data influences these machine learning models. We investigate how data provenance and the quality of training data affect performance, and how well, if at all, trained models generalize across software domains. We focus on the variable renaming problem using one such machine learning model, DIRE . We first describe DIRE in detail and the accompanying technique used to generate training data from raw code. We also evaluate DIRE ’s overall performance without respect to data quality. Next, we show how training on more popular, possibly higher quality code (measured using GitHub stars) leads to a more generalizable model because popular code tends to have more diverse variable names. Finally, we evaluate how well DIRE predicts domain-specific identifiers, propose a modification to incorporate domain information, and show that it can predict identifiers in domain-specific scenarios 23% more frequently than the original DIRE model.

DbGaPCheckup: pre-submission checks of dbGaP-formatted subject phenotype files

BackgroundData archiving and distribution are essential to scientific rigor and reproducibility of research. The National Center for Biotechnology Information’s Database of Genotypes and Phenotypes (dbGaP) is a public repository for scientific data sharing. To support curation of thousands of complex data sets, dbGaP has detailed submission instructions that investigators must follow when archiving their data.ResultsWe developed dbGaPCheckup, an R package which implements a series of check, awareness, reporting, and utility functions to support data integrity and proper formatting of the subject phenotype data set and data dictionary prior to dbGaP submission. For example, as a tool, dbGaPCheckup ensures that the data dictionary contains all fields required by dbGaP, and additional fields required by dbGaPCheckup; the number and names of variables match between the data set and data dictionary; there are no duplicated variable names or descriptions; observed data values are not more extreme than the logical minimum and maximum values stated in the data dictionary; and more. The package also includes functions that implement a series of minor/scalable fixes when errors are detected (e.g., a function to reorder the variables in the data dictionary to match the order listed in the data set). Finally, we also include reporting functions that produce graphical and textual descriptives of the data to further reduce the likelihood of data integrity issues. The dbGaPCheckup R package is available on CRAN (https://CRAN.R-project.org/package=dbGaPCheckup) and developed on GitHub (https://github.com/lwheinsberg/dbGaPCheckup).ConclusiondbGaPCheckup is an innovative assistive and timesaving tool that fills an important gap for researchers by making dbGaP submission of large and complex data sets less error prone.

"The impact of unemployment on cognitive, affective, and eudaimonic well-being facets: Investigating immediate effects and short-term adaptation": Correction.

Reports an error in "The impact of unemployment on cognitive, affective, and eudaimonic well-being facets: Investigating immediate effects and short-term adaptation" by Mario Lawes, Clemens Hetschko, Ronnie Schöb, Gesine Stephan and Michael Eid (Journal of Personality and Social Psychology, Advanced Online Publication, Apr 04, 2022, np). In the original article, the letter "M" in "M1" and "M2" throughout should not be italicized. The word "occurred" is corrected to "occurred" and the word "occurring" is corrected to "occurring" in several places throughout. The acronym LCS in Figure 1 note is corrected to latent change score model. The average immediate effect on life satisfaction that appears in the first sentence of the Cognitive Well-Being section is -4.74. The second sentence of the second paragraph in the Cognitive Well-Being section should appear as Luhman et al. (2014), for example, reported that life satisfaction dropped by 4.1 p.p. The third paragraph in the Cognitive Well-Being section should appear as Moreover, the effects can be compared to international panel studies. Variable names in Figures 2 and 3 notes should appear italicized. All versions of this article have been corrected. (The following abstract of the original article appeared in record 2022-47585-001). While long-lasting declines in life satisfaction following unemployment have been well documented, evidence on the impact of unemployment on affective and eudaimonic well-being is scarce. Moreover, most existing studies relied on yearly panel data and were unable to separate the immediate effects of entering unemployment from prospective effects occurring before individuals become unemployed. The present study identified the immediate effects of entering unemployment on cognitive, affective and eudaimonic well-being facets using a control-group design based on monthly panel data of initially employed German jobseekers who were at high risk of losing their job. In order to investigate patterns of short-term adaptation, the study further examined whether average well-being levels change within the first months of unemployment using a mixed-effects trait-state-occasion model. All effects were separately computed for jobseekers affected by mass-layoffs or plant closures and individuals who registered as jobseekers due to other reasons. Multi-item instruments and experience sampling were used to validly measure the various well-being facets. The results indicate that life satisfaction and income satisfaction significantly decreased for individuals affected by mass-layoffs or plant closures from the last month in employment to the first month in unemployment. For individuals who registered as jobseekers due to other reasons, these effects were smaller and not significant in the case of life satisfaction. Crucially, there were no immediate effects of entering unemployment on the examined affective and eudaimonic well-being facets. Moreover, well-being levels were generally stable within the first months of unemployment indicating a general absence of short-term adaptation. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The impact of unemployment on cognitive, affective, and eudaimonic well-being facets: Investigating immediate effects and short-term adaptation.

[Correction Notice: An Erratum for this article was reported online in Journal of Personality and Social Psychology on Jun 02 2022 (see record 2022-66262-001). In the original article, the letter "M" in "M1" and "M2" throughout should not be italicized. The word "occurred" is corrected to "occurred" and the word "occurring" is corrected to "occurring" in several places throughout. The acronym LCS in Figure 1 note is corrected to latent change score model. The average immediate effect on life satisfaction that appears in the first sentence of the Cognitive Well-Being section is -4.74. The second sentence of the second paragraph in the Cognitive Well-Being section should appear as Luhman et al. (2014), for example, reported that life satisfaction dropped by 4.1 p.p. The third paragraph in the Cognitive Well-Being section should appear as Moreover, the effects can be compared to international panel studies. Variable names in Figures 2 and 3 notes should appear italicized. All versions of this article have been corrected.] While long-lasting declines in life satisfaction following unemployment have been well documented, evidence on the impact of unemployment on affective and eudaimonic well-being is scarce. Moreover, most existing studies relied on yearly panel data and were unable to separate the immediate effects of entering unemployment from prospective effects occurring before individuals become unemployed. The present study identified the immediate effects of entering unemployment on cognitive, affective and eudaimonic well-being facets using a control-group design based on monthly panel data of initially employed German jobseekers who were at high risk of losing their job. In order to investigate patterns of short-term adaptation, the study further examined whether average well-being levels change within the first months of unemployment using a mixed-effects trait-state-occasion model. All effects were separately computed for jobseekers affected by mass-layoffs or plant closures and individuals who registered as jobseekers due to other reasons. Multi-item instruments and experience sampling were used to validly measure the various well-being facets. The results indicate that life satisfaction and income satisfaction significantly decreased for individuals affected by mass-layoffs or plant closures from the last month in employment to the first month in unemployment. For individuals who registered as jobseekers due to other reasons, these effects were smaller and not significant in the case of life satisfaction. Crucially, there were no immediate effects of entering unemployment on the examined affective and eudaimonic well-being facets. Moreover, well-being levels were generally stable within the first months of unemployment indicating a general absence of short-term adaptation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Naming Practices in Object-oriented Programming: An Empirical Study

Currently, research indicates that comprehending code takes up far more developer time than writing code. Given that most modern programming languages place little to no limitations on identifier names, and so developers are allowed to choose identifier names at their own discretion, one key aspect of code comprehension is the naming of identifiers. Research in naming identifiers shows that informative names are crucial to improving the readability and maintainability of programs: essentially, intention-revealing names make code easier to understand and act as a basic form of documentation. Poorly named identifiers tend to hurt the comprehensibility and maintainability of software systems. However, most computer science curricula emphasize programming concepts and language syntax over naming guidelines and conventions. Consequently, programmers lack knowledge about naming practices. This article is an extension of our previous study on naming practices. Previously, we set out to explore naming practices of Java programmers. To this end, we analyzed 1,421,607 identifier names (i.e., attributes, parameters, and variables names) from 40 open-source Java projects and categorized these names into eight naming practices. As a follow-up study to further investigate naming practices, we examined 40 open-source C++ projects and categorized 1,181,774 identifier names according to the previously mentioned eight naming practices. We examined the occurrence and prevalence of these categories across C++ and Java projects and our results also highlight in which contexts identifiers following each naming practice tend to appear more regularly. Finally, we also conducted an online survey questionnaire with 52 software developers to gain insight from the industry. All in all, we believe the results based on the analysis of 2,603,381 identifier names can be helpful to enhance programmers’ awareness and contribute to improving educational materials and code review methods.

Scientific modelling can be accessible, interoperable and user friendly: A case study for pasture and livestock modelling in Spain.

This article describes the adaptation of a non-spatial model of pastureland dynamics, including vegetation life cycle, livestock management and nitrogen cycle, for use in a spatially explicit and modular modelling platform (k.LAB) dedicated to make data and models more interoperable. The aim is to showcase to the social-ecological modelling community the delivery of an existing, monolithic model, into a more modular, transparent and accessible approach to potential end users, regional managers, farmers and other stakeholders. This also allows better usability and adaptability of the model beyond its originally intended geographical scope (the Cantabrian Region in the North of Spain). The original code base (written in R in 1,491 lines of code divided into 13 files) combines several algorithms drawn from the literature in an opaque fashion due to lack of modularity, non-semantic variable naming and implicit assumptions. The spatiotemporal rewrite is structured around a set of 10 namespaces called PaL (Pasture and Livestock), which includes 198 interoperable and independent models. The end user chooses the spatial and temporal context of the analysis through an intuitive web-based user interface called k.Explorer. Each model can be called individually or in conjunction with the others, by querying any PaL-related concepts in a search bar. A scientific dataflow and a provenance diagram are produced in conjunction with the model results for full transparency. We argue that this work demonstrates key steps needed to create more Findable, Accessible, Interoperable and Reusable (FAIR) models beyond the selected example. This is particularly essential in environments as complex as agricultural systems, where multidisciplinary knowledge needs to be integrated across diverse spatial and temporal scales in order to understand complex and changing problems.

Automated variable renaming: are we there yet?

Identifiers, such as method and variable names, form a large portion of source code. Therefore, low-quality identifiers can substantially hinder code comprehension. To support developers in using meaningful identifiers, several (semi-)automatic techniques have been proposed, mostly being data-driven (e.g., statistical language models, deep learning models) or relying on static code analysis. Still, limited empirical investigations have been performed on the effectiveness of such techniques for recommending developers with meaningful identifiers, possibly resulting in rename refactoring operations. We present a large-scale study investigating the potential of data-driven approaches to support automated variable renaming. We experiment with three state-of-the-art techniques: a statistical language model and two DL-based models. The three approaches have been trained and tested on three datasets we built with the goal of evaluating their ability to recommend meaningful variable identifiers. Our quantitative and qualitative analyses show the potential of such techniques that, under specific conditions, can provide valuable recommendations and are ready to be integrated in rename refactoring tools. Nonetheless, our results also highlight limitations of the experimented approaches that call for further research in this field.

Exploring the similarities and differences of variables collected by burn registers globally: protocol for a data dictionary review study

IntroductionBurn registers can provide high-quality clinical data that can be used for surveillance, research, planning service provision and clinical quality assessment. Many countrywide and intercountry burn registers now exist. The...