Coding Tools Research Articles

Methods and means of static and dynamic code analysis

The purpose of the article is to explore the methods and tools used to analyze software code in order to identify errors and potential problems. Static and dynamic code analysis are key processes in software development, as they allow you to detect errors in the early stages of development, reduce the risk of problems later and ensure high quality of the software product. The article discusses various methods and means of static and dynamic code analysis. For each method and tool, examples of their use and advantages and disadvantages are described.
 The article will be useful for software developers who want to improve the quality of their products and reduce the risk of problems. It will provide readers with an in-depth understanding of code analysis techniques and tools and help them choose the most appropriate tool for their needs.

Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison.

Automatic job coding tools were developed to reduce the laborious task of manually assigning job codes based on free-text job descriptions in census and survey data sources, including large occupational health studies. The objective of this study is to provide a case study of comparative performance of job coding and JEM (Job-Exposure Matrix)-assigned exposures agreement using existing coding tools. We compared three automatic job coding tools [AUTONOC, CASCOT (Computer-Assisted Structured Coding Tool), and LabourR], which were selected based on availability, coding of English free-text into coding systems closely related to the 1988 version of the International Standard Classification of Occupations (ISCO-88), and capability to perform batch coding. We used manually coded job histories from the AsiaLymph case-control study that were translated into English prior to auto-coding to assess their performance. We applied two general population JEMs to assess agreement at exposure level. Percent agreement and PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) were used to compare the agreement of results from manual coders and automatic coding tools. The coding per cent agreement among the three tools ranged from 17.7 to 26.0% for exact matches at the most detailed 4-digit ISCO-88 level. The agreement was better at a more general level of job coding (e.g. 43.8-58.1% in 1-digit ISCO-88), and in exposure assignments (median values of PABAK coefficient ranging 0.69-0.78 across 12 JEM-assigned exposures). Based on our testing data, CASCOT was found to outperform others in terms of better agreement in both job coding (26% 4-digit agreement) and exposure assignment (median kappa 0.61). In this study, we observed that agreement on job coding was generally low for the three tools but noted a higher degree of agreement in assigned exposures. The results indicate the need for study-specific evaluations prior to their automatic use in general population studies, as well as improvements in the evaluated automatic coding tools.

Development of a training module for corporate online platforms using no code tools

Development of a training module for corporate online platforms using no code tools

Code Generation by Example Using Symbolic Machine Learning

Code generation is a key technique for model-driven engineering (MDE) approaches of software construction. Code generation enables the synthesis of applications in executable programming languages from high-level specifications in UML or in a domain-specific language. Specialised code generation languages and tools have been defined; however, the task of manually constructing a code generator remains a substantial undertaking, requiring a high degree of expertise in both the source and target languages, and in the code generation language. In this paper, we apply novel symbolic machine learning techniques for learning tree-to-tree mappings of software syntax trees, to automate the development of code generators from source–target example pairs. We evaluate the approach on several code generation tasks, and compare the approach to other code generator construction approaches. The results show that the approach can effectively automate the synthesis of code generators from examples, with relatively small manual effort required compared to existing code generation construction approaches. We also identified that it can be adapted to learn software abstraction and translation algorithms. The paper demonstrates that a symbolic machine learning approach can be applied to assist in the development of code generators and other tools manipulating software syntax trees.

Android Source Code Vulnerability Detection: A Systematic Literature Review

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not address the security aspects appropriately. This is often due to a lack of automated mechanisms to identify, test, and fix source code vulnerabilities at the early stages of design and development. Therefore, the need to fix such issues at the initial stages rather than providing updates and patches to the published applications is widely recognized. Researchers have proposed several methods to improve the security of applications by detecting source code vulnerabilities and malicious codes. This Systematic Literature Review (SLR) focuses on Android application analysis and source code vulnerability detection methods and tools by critically evaluating 118 carefully selected technical studies published between 2016 and 2022. It highlights the advantages, disadvantages, applicability of the proposed techniques, and potential improvements of those studies. Both Machine Learning (ML)-based methods and conventional methods related to vulnerability detection are discussed while focusing more on ML-based methods, since many recent studies conducted experiments with ML. Therefore, this article aims to enable researchers to acquire in-depth knowledge in secure mobile application development while minimizing the vulnerabilities by applying ML methods. Furthermore, researchers can use the discussions and findings of this SLR to identify potential future research and development directions.

Equal Value String and Copy Above String Based String Prediction for SCC in AVS3

String prediction (SP) is a very efficient screen content coding (SCC) tool which has been adopted in the third generation of Audio Video Standard (AVS3). It is observed that two special types of strings occur frequently. To further improve the coding efficiency for SCC on top of the original SP, a new variation of SP named Equal-value-string and Copy-above-string based SP (ECSP) is proposed. An ECSP coding unit uses only three types of strings: Equal-value-string, Copy-above-string, and Unpredictable-pixel-string. Compared with the AVS3 reference software HPM9.0 with ECSP disabled, using AVS3 SCC Common Test Condition and YUV 4:2:0 test sequences, the proposed technique achieves an average Y BD-rate reduction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5.54</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3.01%</i> for All Intra and Low Delay configurations, respectively, with low additional encoding and decoding complexity. The proposed ECSP has been adopted in the AVS3 standard.

AdubaPE - aplicativo low code para auxiliar agricultores na adubação e correção de solos em Pernambuco

Information and communication technology has been gaining more and more space in the management of city and rural problems, bringing efficiency, agility in processes and improving results and people's quality of life. Access to applications, social networks, and IoT (Internet of Things) devices has been intensifying and becoming increasingly popular, enabling the inclusion of more people in the digital world. The objective of this work was to develop a low code application that allows farmers, based on analytical reports of the soil, to verify the recommendation of fertilization and liming necessary for the crop to be planted. The technical data on fertilization and liming for the various crops planted in the state were obtained from the tables of recommendations for fertilizers and soil amendments for the state of Pernambuco, as Brazil, due to its large territorial dimension, has different types of soils, as well as predominant types of crops by region. The low code tool used was kodular, developed in MIT App Inventor to facilitate the coding of applications. This product is expected to provide opportunities for farmers in the state to minimize costs and increase productivity, as well as help protect the environment, since the exaggerated supply of fertilizers and soil amendments can lead to contamination, as well as surface and underground water with damage to the main uses. It is expected that food production aligned with sustainable innovation can contribute to improving the quality of life for people and the environment.

On the use of free code tools to simulate the propagation of radiation following dirty bomb explosions in sensible contexts

On the use of free code tools to simulate the propagation of radiation following dirty bomb explosions in sensible contexts

Вектор Штера: знание в координатах города

The “Stehr vector” is a metaphor associated with the name of the modern philosopher and sociologist of science Niko Stehr and designating the trajectory of movement towards a “knowledge society”, where intellectual achievements as a public good have the highest value – they are priceless and, therefore, are not sold or bought, and access to them is free. Since a number of obstacles are characteristic to the formation of the ascent to the knowledge society, the main one is the usurpation of cognitive resources by digital giants with the help of legal coding tools for intellectual products. Knowledge is expropriated from its creators – scientists – and then sold, thereby further enriching the “knowledge monopolists”. The “Stehr vector”, overcoming such barriers, is aimed at the future, where different forms of social exchange are developing on the model of social communications among representatives of the “creative class” (R. Florida), concentrated in the centers of knowledge – cities with great scientific and educational potential. Since the “city of knowledge” is a cognitive generator, as a result, the ability to think scientifically becomes a property of every person. The article shows that certain analogies are possible between “cities of knowledge” as a “new utopia” developing in the algorithms of the “Stehr vector”, and the first university cities in Medieval Europe, which arose and developed as accumulators of knowledge and university life. These “new Middle Ages” promise to be more intellectually free than modern knowledge capitalism. Thus, the “Stern vector” makes it possible to design not the Berdyaev dystopian version of the “New Middle Ages”, but, on the contrary, more intellectually free version than modern knowledge capitalism.

The Impact of Nurse Health-Coaching Strategies on Cognitive-Behavioral Outcomes in Older Adults.

The practice of nurse health coaching (NHC) draws from the art and science of nursing, behavioral sciences, and evidence-based health-coaching methods. This secondary analysis of the audio-recorded natural language of participants during NHC sessions of our recent 8-week RCT evaluates improvement over time in cognitive−behavioral outcomes: change talk, resiliency, self-efficacy/independent agency, insight and pattern recognition, and building towards sustainability. We developed a measurement tool for coding, Indicators of Health Behavior Change (IHBC), that was designed to allow trained health-coach experts to assess the presence and frequency of the indicators in the natural language content of participants. We used a two-step method for randomly selecting the 20 min audio-recorded session that was analyzed at each time point. Fifty-six participants had high-quality audio recordings of the NHC sessions. Twelve participants were placed in the social determinants of health (SDH) group based on the following: low income (<USD 20,000/year), early-onset hypertension, and social disadvantages. Our analyses significantly improved change talk and the other four factors over time. Our factor analyses indicated two distinct factors at each measurement point of the study, demonstrating the stability of the outcome measures over time. Our newly developed measurement tool, IHBC, proved stable in structure over time and sensitive to change. This NHC program shows promise in improving cognitive−behavioral indicators associated with health behavior change in both non-SDH and SDH individuals.

Multitask Learning Based Intra-Mode Decision Framework for Versatile Video Coding

In mid-2020, the new international video coding standard, namely versatile video coding (VVC), was officially released by the Joint Video Expert Team (JVET). As its name indicates, the VVC enables a higher level of versatility with better compression performance compared to its predecessor, high-efficiency video coding (HEVC). VVC introduces several new coding tools like multiple reference lines (MRL) and matrix-weighted intra-prediction (MIP), along with several improvements on the block-based hybrid video coding scheme such as quatree with nested multi-type tree (QTMT) and finer-granularity intra-prediction modes (IPMs). Because finding the best encoding decisions is usually preceded by optimizing the rate distortion (RD) cost, introducing new coding tools or enhancing existing ones requires additional computations. In fact, the VVC is 31 times more complex than the HEVC. Therefore, this paper aims to reduce the computational complexity of the VVC. It establishes a large database for intra-prediction and proposes a multitask learning (MTL)-based intra-mode decision framework. Experimental results show that our proposal enables up to 30% of complexity reduction while slightly increasing the Bjontegaard bit rate (BD-BR).

Investigation of Teacher's Role and Task on the Basis of Social Emotional Learning in After-School Coding Courses

After-school courses include programs in which students participate voluntarily according to their wishes and interests. In after-school coding course, which is one of these programs, block-based coding tools are studied and practiced in small groups in general. These courses have many positive effects, such as increasing the students' school performance and supporting their positive attitudes. In addition, social emotional learning, which covers the features desired by today's business world, can be supported by these courses. In literature, there are studies on the role and tasks of the teacher in formal education; however, there are no studies on determining the roles and tasks of the teacher in after-school courses. This study aims to examine the role and tasks of the teacher in the coding courses after school based on social emotional learning. For this purpose, one-on-one and focus group interviews were conducted with seven teachers who gave after-school coding courses. Content and frame analysis were performed on the obtained data, and three themes emerged: peer relations, self-regulation, and task consciousness. As a result of the study, it was seen that teachers fulfill the roles of observer, communicator, manager, counselor, organizer, and supporter.

WHO Emergency Medical Teams Minimal Data Set in Conflict-Stricken Ukraine: Comparative Analysis of a New Primary Health Care Coding Tool

Background/Introduction:The WHO EMT Minimum Data Set (EMT-MDS) was designed for data collection in sudden-onset disasters. Using EMT-MDS in the context of primary health care (PHC) generated large quantities of low granularity data that threatened the successful delivery of UK-Med’s clinical programs in Ukraine. Accordingly, UK-Med developed, piloted, and implemented a new coding tool (PHC-CT) tailored to PHC presentations prevalent in humanitarian settings.Objectives:To assess the performance of EMT-MDS and PHC-CT in the generation of programmatically-useful diagnostic codes from data collected in mobile PHC clinics in Ukraine during active conflict.To compare the performance of EMT-MDS and PHC-CT in this setting and to suggest recommendations for data collection tool improvements.Method/Description:After multiple iterations, the final version of PHC-CT was used to collect clinical data from all UK-Med clinical encounters in Ukraine from March 28, 2022-May 13, 2022. Clinical data using EMT-MDS were collected simultaneously. The prevalence of each diagnostic code was calculated using both EMT-MDS and PHC-CT, expressed as a proportion of the total diagnoses, and compared between the two coding tools.Results/Outcomes:1,390 clinical encounters took place during the study. Data coded using EMT-MDS generated 1,756 diagnoses (86.8% of total diagnoses) categorized as “Other Diagnosis” while the same data coded using PHC-CT generated 37 diagnoses (1.8% of total diagnoses) categorized as “Other Diagnosis.” Only seven of the available 25 diagnostic codes in EMT-MDS were used, while 48 of the 67 available diagnostic codes in PHC-CT were used.Conclusion:PHC-CT offers substantial benefits beyond those provided by EMT-MDS when utilized in mobile PHC clinics in humanitarian settings.Tables and Figures (optional)Table 1.Number of Clinical Encounters, Unique Diagnoses, and Frequency of Selected Diagnostic Codes for EMT-MDS and PHC-CT. (Note: % refers to proportion of diagnoses made)

Overview of the Low Complexity Enhancement Video Coding (LCEVC) Standard

The Low Complexity Enhancement Video Coding (LCEVC) specification is a recent standard approved by the ISO/IEC JTC 1/SC 29/WG04 (MPEG) Video Coding. The main goal of LCEVC is to provide a standalone toolset for the enhancement of any other existing codec. It works on top of other coding schemes, resulting in a multi-layer video coding technology, but unlike existing scalable video codecs, adds enhancement layers completely independent from the base video. The LCEVC technology takes as input the decoded video at lower resolution and adds up to two enhancement sub-layers of residuals encoded with specialized low-complexity coding tools, such as simple temporal prediction, frequency transform, quantization, and entropy encoding. This paper provides an overview of the main features of the LCEVC standard: high compression efficiency, low complexity, minimized requirements of memory and processing power.

An Adjacency Encoding Information-Based Fast Affine Motion Estimation Method for Versatile Video Coding

Versatile video coding (VVC), a new generation video coding standard, achieves significant improvements over high efficiency video coding (HEVC) due to its added advanced coding tools. Despite the fact that affine motion estimation adopted in VVC takes into account the translational, rotational, and scaling motions of the object to improve the accuracy of interprediction, this technique adds a high computational complexity, making VVC unsuitable for use in real-time applications. To address this issue, an adjacency encoding information-based fast affine motion estimation method for VVC is proposed in this paper. First, this paper counts the probability of using the affine mode in interprediction. Then we analyze the trade-off between computational complexity and performance improvement based on statistical information. Finally, by exploring the mutual exclusivity between skip and affine modes, an enhanced method is proposed to reduce interprediction complexity. Experimental results show that compared with the VVC, the proposed low-complexity method achieves 10.11% total encoding time reduction and 40.85% time saving of affine motion estimation with a 0.16% Bjøontegaard delta bitrate (BDBR) increase.

TaskLint: Automated Detection of Ambiguities in Task Instructions

Clear instructions are a necessity for obtaining accurate results from crowd workers. Even small ambiguities can force workers to choose an interpretation arbitrarily, resulting in errors and inconsistency. Crisp instructions require significant time to design, test, and iterate. Recent approaches have engaged workers to detect and correct ambiguities. However, this process increases the time and money required to obtain accurate, consistent results. We present TaskLint, a system to automatically detect problems with task instructions. Leveraging a diverse set of existing NLP tools, TaskLint identifies words and sentences that might foretell worker confusion. This is analogous to static analysis tools for code ("linters"), which detect possible features in code that might indicate the presence of bugs. Our evaluation of TaskLint using task instructions created by novices confirms the potential for static tools to improve task clarity and the accuracy of results, while also highlighting several challenges.

Exploring the Impact of Language Concordance on Cancer Communication.

Patients with cancer who have limited English proficiency are more likely to experience inequities in cancer knowledge, timely care, and access to clinical trials. Matching patients with language-concordant clinicians and working with professional interpreters can effectively reduce language-related disparities, but little data are available regarding the impact of language-concordant interactions in oncology care. This study aimed to assess the use of the Roter Interaction Analysis System (RIAS) in language-concordant and -discordant interactions for patients with non-English language preference presenting for an initial oncology visit at four New York City hospitals. We used the RIAS, a validated tool for qualitative coding and quantitative analysis, to evaluate interactions between 34 patients and 16 clinicians. The pairings were stratified into dyads: English language-concordant (n = 12); professionally interpreted (n = 11); partially language-concordant (n = 4, partially bilingual clinicians who communicated in Spanish and/or used ad hoc interpreters); and Spanish language-concordant (n = 7). A trained Spanish-speaking coder analyzed the recordings using established RIAS codes. Spanish language-concordant clinicians had almost two-fold greater number of statements about biomedical information than English language-concordant clinicians. Spanish language-concordant patients had a higher tendency to engage in positive talk such as expressing agreement. The number of partnership/facilitation-related statements was equivalent for English and Spanish language-concordant groups but lower in professionally interpreted and partially language-concordant dyads. Language concordance may facilitate more effective biomedical counseling and therapeutic relationships between oncology clinicians and patients. Future research should further explore the impact of language concordance on cancer-specific health outcomes.

The Exoplanet Modeling and Analysis Center at NASA Goddard

The Exoplanet Modeling and Analysis Center (EMAC) at NASA Goddard Space Flight Center is a web-based catalog, repository, and integration platform for modeling and analysis resources focused on the study of exoplanet characteristics and environments. EMAC hosts user-submitted resources ranging in category from planetary interior models to data visualization tools. Other features of EMAC include integrated web tools developed by the EMAC team in collaboration with the tools’ original author(s) and video demonstrations of a growing number of hosted tools. EMAC aims to be a comprehensive repository for researchers to access a variety of exoplanet resources that can assist them in their work, and currently hosts a growing number of code bases, models, and tools. EMAC is a key project of the NASA GSFC Sellers Exoplanet Environments Collaboration and can be accessed at https://emac.gsfc.nasa.gov.

Versatile Video Coding Standard: A Review From Coding Tools to Consumers Deployment

The amount of video content and the number of applications based on multimedia information increase each day. The development of new video coding standards is a challenge to increase the compression rate and other important features with a reasonable increase in the computational load. The Joint Video Experts Team of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group (MPEG) have worked together to develop the versatile video coding standard, finalized in July 2020 as the International Standard 23090-3 (MPEG-I Part 3). This article overviews some interesting consumer electronic use cases, the compression tools described in the standard, the current available real-time implementations, and the first industrial trials done with this standard.

Addressing alcohol use among blood pressure patients in Thai primary care: Lessons from a survey-based stakeholder consultation

Alcohol use is a major risk factor for noncommunicable diseases in Thailand, and one of its pathways is high blood pressure. Given that brief intervention can effectively reduce hazardous alcohol consumption, this study aimed to investigate how hypertensive patients with concomitant alcohol use are identified and treated in Thai primary care settings and what this may mean for screening and lifestyle intervention strategies. In a cross-sectional, mixed-method design, we surveyed 91 participants from three different groups of Thai stakeholders: policy- and decisionmakers; healthcare practitioners; and patients diagnosed with hypertension. Data was collected between December 2020 and May 2021. Responses were analyzed descriptively and using open coding tools to identify current practices, barriers, facilitators, and implications for interventions. All stakeholder groups regarded alcohol use as an important driver of hypertension. While lifestyle interventions among hypertensive patients were perceived as beneficial, current lifestyle support was limited. Barriers included limited resources in primary healthcare facilities, lack of continuous monitoring or follow-up, missing tools or procedures for risk assessment and lifestyle intervention, and stigmatization of alcohol use. Our results suggest that although screening for lifestyle risk factors (including alcohol use) and lifestyle interventions are not yet sufficiently established, a wide range of stakeholders still recognize the potential of interventions targeted at hazardous alcohol use among hypertensive patients. Future interventions may establish standardized assessment tools, be tailored to high-risk groups, and include electronic or remote elements.