Code Changes Research Articles

Vehicular Traffic Flow Detection and Monitoring for Implementation of Smart Traffic Light: A Case Study for Road Intersection in Limeira, Brazil

This paper proposes the development of a smart traffic light prototype based on vehicular traffic flow measurement in the stretch between two avenues in the city of Limeira, SP, Brazil, focusing on the stretch towards UNICAMP’s School of Technology. To this end, we initially developed a Python code using the OpenCV library in order to detect and count vehicles. With the counting in operation, programming logic was inserted, aiming at preparing traffic light timers based on vehicular traffic. Finally, the traffic lights were added to display video via a code change to show the ongoing color changes, also obtaining a code for identifying vehicles and flow, in addition to the virtual traffic light system itself in the system. Vehicle counting accuracy was 75% for large vehicles, 90% for passenger cars, and 100% for motorcycles. The simulation of a smart traffic light implementation worked satisfactorily according to the post-processing of the video recorded for validation.

Towards Better Comprehension of Breaking Changes in the NPM Ecosystem

Code evolution is prevalent in software ecosystems, which can provide many benefits, such as new features, bug fixes, security patches, etc., while still introducing breaking changes that make downstream projects fail to work. Breaking changes cause a lot of effort to both downstream and upstream developers: downstream developers need to adapt to breaking changes and upstream developers are responsible for identifying and documenting them. In the NPM ecosystem, characterized by frequent code changes and a high tolerance for making breaking changes, the effort is larger. For better comprehension of breaking changes in the NPM ecosystem and to enhance breaking change detection tools, we conduct a large-scale empirical study to investigate breaking changes in the NPM ecosystem. We construct a dataset of explicitly documented breaking changes from 381 popular NPM projects. We find that 95.4% of the detected breaking changes can be covered by developers’ documentation, and 19% of the breaking changes cannot be detected by regression testing. Then in the process of investigating source code of our collected breaking changes, we yield a taxonomy of JavaScript and TypeScript-specific syntactic breaking changes and a taxonomy of major types of behavioral breaking changes. Additionally, we investigate the reasons why developers make breaking changes in NPM and find three major reasons, i.e., to reduce code redundancy, to improve identifier names, and to improve API design, and each category contains several sub-items. We provide actionable implications for future research, e.g., automatic naming and renaming techniques should be applied in JavaScript projects to improve identifier names, future research can try to detect more types of behavioral breaking changes. By presenting the implications, we also discuss the weakness of automatic renaming and breaking change detection approaches, such as the lack of support for public identifiers and various types of breaking changes.

Speech perception and language comprehension in primary progressive aphasia

Primary progressive aphasia (PPA) is a neurodegenerative disorder characterized by progressive loss of speech and language. Although speech perception and language comprehension deficits are observed in individuals with PPA, these deficits have been understudied relative to production deficits. Recent work has examined receptive language processing at sublexical, lexical, and semantic levels in PPA; however, systematic investigation of these levels of processing within a single PPA cohort is lacking. The current study sought to fill this gap. Individuals with logopenic, nonfluent, and semantic variants of PPA and healthy, age-matched controls completed minimal pairs syllable discrimination, auditory lexical decision, and picture-word verification tasks to assess sublexical, lexical, and semantic processing. Distinct profiles were observed across PPA variants. Individuals with logopenic variant PPA had impaired performance on auditory lexical decision and picture-word verification tasks, with a trend toward impaired performance on the syllable discrimination task. Individuals with nonfluent and semantic variant PPA had impaired performance only on auditory lexical decision and picture-word verification. Evaluation of the types of errors made on the picture-word verification task (phonological and semantic) provided further insight into levels of deficits across the variants. Overall, the results indicate deficits in receptive processing at the lexical-phonological, lexical-semantic, and semantic levels in logopenic variant PPA, with a trend toward impaired sublexical processing. Deficits were observed at the lexical-semantic and semantic levels in semantic variant PPA, and lexical-phonological deficits were observed in nonfluent PPA, likely reflecting changes both in lexical-phonological processing as well as changes in predictive coding during perception. This study provides a more precise characterization of the linguistic profile of each PPA subtype for speech perception and language comprehension. The constellation of deficits observed in each PPA subtype holds promise for differential diagnosis and for informing models of intervention.

From GPU to CPU (and Beyond): Extending Hardware Support in GPUSPH Through a SYCL‐Inspired Interface

ABSTRACTWhile most software is originally designed for serial or parallel execution on CPU, and porting to GPU comes later in its development, GPUSPH was designed from the ground up to run on GPUs using CUDA. Making it accessible to a wider audience by introducing support for other computational hardware, and in particular CPUs, poses challenges that are complementary to the ones normally faced when porting CPU code to GPU. We present the approach we have adopted to support CPUs as computational devices in GPUSPH with minimal code changes and low developer effort. Detailed benchmarks illustrating the performance of the implementation and its scalability across multiple cores in both single‐socket and NUMA configurations show good strong and weak scaling.

Continuous Integration and Continuous Deployment (CI/CD) Optimization

The advent of Continuous Integration and Continuous Deployment (CI/CD) has fundamentally altered the landscape of software development, enabling teams to deliver updates with unprecedented speed and reliability. By automating the integration of code changes from multiple developers into a central repository, CI/CD practices ensure that software is continuously tested and deployed. This ongoing cycle not only facilitates quicker release cycles but also enhances collaboration among team members and fosters a culture of shared responsibility for code quality. Despite these advancements, organizations face significant challenges in optimizing their CI/CD pipelines. As software systems grow in complexity, the demand for swift and dependable deployments intensifies. This paper explores various techniques and strategies for optimizing CI/CD processes to minimize deployment times while maintaining system reliability. Key optimization methods discussed include: Parallelization of Build Processes: This technique involves breaking down the build process into smaller, independent tasks that can be executed concurrently. By leveraging distributed computing resources, organizations can significantly reduce build times, allowing for faster iterations and deployments. Dependency Caching: Caching dependencies can drastically decrease build times by reusing previously downloaded components. This approach not only speeds up the build process but also minimizes network load and enhances the overall efficiency of the CI/CD pipeline. Incremental Builds: Unlike full builds that compile the entire codebase, incremental builds focus on compiling only the changes made since the last build. This strategy reduces the amount of work needed for each build, accelerating the overall development process. The paper also delves into advanced rollback mechanisms such as blue-green deployments and canary releases. Blue-green deployments allow teams to maintain two identical production environments, enabling smooth transitions and quick rollbacks in case of issues. Canary deployments, on the other hand, introduce new features to a small subset of users before a full rollout, allowing teams to monitor the impact and catch potential failures early. Additionally, automated rollback mechanisms play a vital role in maintaining system reliability, ensuring that any failed deployments can be reverted swiftly to avoid downtime and user disruption. The impact of automation tools on deployment speed and error reduction is another critical aspect examined in this research. Automation frameworks can streamline various stages of the CI/CD process, from code integration to testing and deployment, minimizing human error and ensuring consistent, repeatable processes. By implementing robust automation strategies, organizations can not only accelerate their deployment cycles but also improve overall software quality.

The upper bound of information diffusion in code review

BackgroundCode review, the discussion around a code change among humans, forms a communication network that enables its participants to exchange and spread information. Although reported by qualitative studies, our understanding of the capability of code review as a communication network is still limited.ObjectiveIn this article, we report on a first step towards understanding and evaluating the capability of code review as a communication network by quantifying how fast and how far information can spread through code review: the upper bound of information diffusion in code review.MethodIn an in-silico experiment, we simulate an artificial information diffusion within large (Microsoft), mid-sized (Spotify), and small code review systems (Trivago) modelled as communication networks. We then measure the minimal topological and temporal distances between the participants to quantify how far and how fast information can spread in code review.ResultsAn average code review participants in the small and mid-sized code review systems can spread information to between 72 % and 85 % of all code review participants within four weeks independently of network size and tooling; for the large code review systems, we found an absolute boundary of about 11 000 reachable participants. On average (median), information can spread between two participants in code review in less than five hops and less than five days.ConclusionWe found evidence that the communication network emerging from code review scales well and spreads information fast and broadly, corroborating the findings of prior qualitative work. The study lays the foundation for understanding and improving code review as a communication network.

Historical and Current Perspectives on the Criminal Codes as Guarantees of Legal Security in Slovakia

The author describes two crucial periods in Slovak legal history from the perspective of the principle of legal security in criminal law. Firstly, the 19th-century Criminal Code, known as the Csemegi Code, is at the center of attention as it ended the centuries-long legal insecurity resulting from the lack of criminal law codification. The author analyses its leading principles as the safeguard of legal security. In contrast, the second part of the article focuses on the second codification of criminal law in Slovak legal history, the Criminal Code of 1950. The author demonstrates how the totalitarian regime destroyed the principle of legal security in criminal law in Slovakia, only slowly rebuilding it after the death of Joseph Stalin through amendments in 1956 and new legislation in 1961. The current Slovak Criminal Code and Criminal Procedure Code have been in effect since 2006. In conclusion, the author draws attention to how the latest changes in criminal law codes caused turbulences and worries over the principle of legal security in Slovakia.

Influencia de las propiedades mecánicas del acero de alta resistencia en el comportamiento de muros de concreto

The behavior of reinforced concrete walls with high-strength steel (yield strength greater than or equal to 550 MPa [80 ksi]) is evaluated. The influence of yield stress (fy) and the tensile-to-yield strength (ft/fy) are analyzed by means of tests on T-shaped concrete walls. Additionally, the effects of uniform elongation (Esu) and fracture elongation (Esf) are evaluated. Two walls reinforced with Grade 690 or 830 steel (100 or 120) (fy = 690 or 830 MPa [100 or 120 ksi]) and with concrete strength of 55 MPa (8 ksi). Test results were compared with previous tests to validate changes in the Concrete Design Code (ACI 318). The data obtained suggest that walls with high-strength steel have a deformation capacity similar to walls reinforced with conventional steel (fy = 420 MPa [60 ksi]). However, the steel must satisfy ft/fy≥1.2,Esu≤6%yEsf≥10% for the walls to exhibit satisfactory behavior. ILIA: Investigaciones Latinoamericanas en Ingeniería y Arquitectura, No. 01, 2024: 91-99.

CI/CD model for optimizing software deployment in SMEs

Small and Medium-sized Enterprises (SMEs) face unique challenges in software deployment, often dealing with limited resources, budget constraints, and the need for fast, reliable updates. This review outlines a Continuous Integration and Continuous Deployment (CI/CD) model specifically tailored to optimize software deployment in SME environments. The proposed CI/CD framework enhances operational efficiency by automating the integration, testing, and deployment processes, significantly reducing the risk of human error and deployment failures. The model leverages key CI/CD practices, including automated testing, version control, and continuous monitoring, to ensure seamless updates and minimal downtime. By integrating tools like Jenkins, GitLab, and Docker, SMEs can streamline code integration, enhance collaboration between development and operations teams, and enable faster, more frequent releases. Automated testing ensures that code changes are validated before deployment, identifying and addressing potential issues early in the development cycle. The framework also emphasizes rollback capabilities and monitoring systems to further reduce deployment risks. Rollbacks allow for quick recovery in case of failure, while continuous monitoring enables real-time detection of performance issues, helping SMEs maintain operational uptime and minimize service disruptions. Moreover, the model supports a microservices architecture, allowing SMEs to scale their applications efficiently without compromising stability. Incorporating CI/CD practices fosters agility, enabling SMEs to respond rapidly to market demands and customer feedback. This model not only improves software quality and deployment speed but also aligns with the budgetary and operational limitations of SMEs. It provides a cost-effective solution that enhances overall system reliability and reduces the frequency of software-related incidents. This CI/CD model serves as a practical approach to software deployment, empowering SMEs to optimize their software development lifecycle while ensuring continuous improvement and operational stability.

Automated reporting and statistical analysis of test case results in continuous integration: A custom dashboard approach

CI has been one of the cornerstones of modern software development, where teams integrate code changes frequently and have automated testing to receive fast feedback. Running a large amount of test data, however, is overwhelming and can be fatiguing in extracting meaningful insights from traditional reporting methods like email notifications. The paper presents a bespoke dashboard, designed to support the extension of automation in test case result reporting and analysis within a CI environment by real-time data visualization, interactivity in filtering and sorting, and statistical insights that would make trends and anomalies easier for teams to detect. Implemented in a large software project, this solution brought immense improvement in decision-making, team collaboration, and efficiency, since the dashboard reduced time spent on manual report analyses and generations. The flexibility and ability to integrate with a number of CI tools make it a powerful solution for addressing traditional reporting shortcomings and let development teams focus on areas needing improvement, ultimately enhancing software quality.

Trends in US Pediatric Asthma Hospitalizations, by Race and Ethnicity, 2012-2020.

Some racial and ethnic minority communities have long faced a higher asthma burden than non-Hispanic White communities. Prior research on racial and ethnic pediatric asthma disparities found stable or increasing disparities, but more recent data allow for updated analysis of these trends. Using 2012-2020 National Inpatient Sample data, we estimated the number of pediatric asthma hospitalizations by sex, age, and race and ethnicity. We converted these estimates into rates using data from the US Census Bureau and then conducted meta-regression to assess changes over time. Because the analysis spanned a 2015 change in diagnostic coding, we performed separate analyses for periods before and after the change. We also excluded 2020 data from the regression analysis. The number of pediatric asthma hospitalizations decreased over the analysis period. Non-Hispanic Black children had the highest prevalence (range, 9.8-36.7 hospitalizations per 10,000 children), whereas prevalence was lowest among non-Hispanic White children (range, 2.2-9.4 hospitalizations per 10,000 children). Although some evidence suggests that race-specific trends varied modestly across groups, results overall were consistent with a similar rate of decrease across all groups (2012-2015, slope = -0.83 [95% CI, -1.14 to -0.52]; 2016-2019, slope = -0.35 [95% CI, -0.58 to -0.12]). Non-Hispanic Black children remain disproportionately burdened by asthma-related hospitalizations. Although the prevalence of asthma hospitalization is decreasing among all racial and ethnic groups, the rates of decline are similar across groups. Therefore, previously identified disparities persist. Interventions that consider the specific needs of members of disproportionately affected groups may reduce these disparities.

Is It Hard to Generate Holistic Commit Message?

Commit messages are important for developers to understand the content and the reason for code changes. However, poor and even empty commit messages widely exist. To improve the quality of commit messages and development efficiency, many commit message generation methods have been proposed. Nevertheless, previous methods mainly focus on a brief generation problem, where both the input code change and the output commit messages are restricted to short. This may initiate a debate on the performance of these methods in practice. In this paper, we attempt to remove the restrictions and move the needle forward to a holistic commit message generation problem. In particular, we conduct experiments to evaluate the performance of existing commit message generation methods in holistic commit message generation. In the experiments, we choose seven state-of-the-art commit generation methods and focus on two important scenarios in commit message generation (i.e., the within-project scenario and the cross-project scenario). To conduct our experiments, we publish a holistic commit message dataset HORDA with test data manually labeled. In our evaluations, we find that in generating holistic commit messages, the IR-based method has a better performance than non-pre-trained generation-based methods in the within-project scenario, contradicting previous research findings. Further, while the pre-trained generation-based methods are better than non-pre-trained generation-based methods, they are still constrained by the limitations of generation models.

Implementing Continuous Integration/Continuous Deployment (CI/CD) Pipelines for Large-Scale iOS Applications

Continuous integration and continuous deployment pipelines have become essential components of modern software development, particularly in creating large-scale iOS apps. The automation of procedures for creating, testing, and deploying is facilitated by them, which results in an increase in both the speed and reliability of the release process. The complexity and scope of the codebase, the need for rigorous testing across a broad range of devices and operating system versions, and the regular necessity of updates to address bugs or bring new features all contribute to the conclusion that continuous integration and continuous delivery pipelines are an integral component of major iOS projects. The integration of continuous integration with continuous delivery provides developers with the assurance that code changes are automatically tested and delivered, hence lowering the need for human intervention, and reducing the chance of mistakes. In addition, this arrangement makes it possible to do parallel testing, which is useful when working with many test cases and device configurations. It is also possible to design continuous integration and continuous delivery pipelines to do tests for code quality, security scans, or other automated checks, which will ensure that the codebase is not compromised.By increasing communication among development teams, continuous integration and continuous delivery pipelines in big iOS projects provide yet another key advantage. It guarantees that every member of the team always has access to the most recent version of the code, which is essential in situations when many teams are working on various aspects or components of the application. In addition, continuous integration and continuous delivery pipelines enhance software quality by accelerating feedback loops, which enables engineers to resolve problems more expediently. Nevertheless, the establishment of continuous integration and continuous delivery pipelines for large-scale iOS apps involves several issues, such as the management of build times, the certification of compatibility with various devices, and the upkeep of the infrastructure. It is necessary to carefully design and optimize the CI/CD procedures to overcome these problems. This may be accomplished by choosing the appropriate tools and configurations adapted to the project's requirements.

Sensitive Data Flagging within Data Quality reports: R and Regex Integration for Effective Text Flagging in Large Datasets

ObjectiveWe’re introducing a comprehensive approach to enhance sensitive data flagging within our Data Quality (DQ) tool. Within de-identified health records, sensitive data such as user ids, post codes, phone numbers, and addresses pose significant privacy risks if exposed in their raw form. To address this challenge, we propose using all necessary regex patterns and free text field checks for sensitive data flagging with the objective of efficient detection within large datasets and ultimately reporting such data. ApproachA regex map has been developed along free text field thresholds, distribution counts and percentage checks to allow a generic approach. This ensures that any new regexes can be added to the flagging process without significant code changes. Once identified, sensitive data instances, including fields that contain free text are flagged and displayed in an html report that is human readable by DQ reviewers. ResultsThrough a combination of SQL and R regex based text processing, our approach allows a seamless identification and flagging of sensitive data within datasets. Sensitive data for large tables of +25 million records with over 50 columns are getting flagged in less than 150 seconds (approx. 2 minutes). ConclusionOur development offers a practical solution for sensitive data flagging in complex datasets where team members can implement robust sensitive data flagging by adding more regex formulas in the codebase. ImplicationsDQ reviewers can inspect the sensitive flagged fields in a human readable way, providing an extra measure of confidence when determining the quality of de-identified health records.

Urban Trail Exposure in Winnipeg, Canada: Longitudinal Cohort Development for a 20-Year Difference-in-Differences Analysis of a Natural Experiment with Varied Duration of Exposure Times

ObjectiveDevelop a cohort to evaluate a real-world experiment of the built environment on the incidence of disease, accounting for movement of people, postal codes, incidence of disease and changing demographics. ApproachThe City of Winnipeg, Canada built multi-use trails in 2010-2012. We used the administrative data housed at the Manitoba Centre for Health Policy to evaluate the effect of this change in the built environment on the incidence of cardiovascular disease events (CVDE) and risk factors (CVDRF) before (2000-2009) and after (2012-2019) the trails were built. The Manitoba Health Insurance Registry contains postal code and demographic information on nearly all city residents and was utilized for cohort creation and person-time exposure. Individuals’ residential postal codes were linked to geo-spatial data to determine proximity to built trails at 400m, 800m and 1200m. Semi-annual postal code changes accounted for movement within/outside city limits or exclusion from the cohort. Diagnoses from hospital abstract and physician visit data and outpatient prescription dispensations were used to access prevalence and incidence of a CVDE composite measure of congestive heart failure, ischemic heart disease and stroke and a CVDRF composite measure of diabetes, dyslipidemia and hypertension. Conclusions and ImplicationsLeveraging a diverse set of administrative databases, we built a cohort evaluate the effect of building multi-use trails in Winnipeg on the reduction on CVDE and CVDRF. This demonstrates how administrative data can be used to evaluate natural, real-world experiments with minimal direct data measurement or public intrusion, resulting in actionable results to inform public policy.

ICD-10-CM Update Preview: Code Changes for October 2024

<i>ICD-10-CM</i> Update Preview: Code Changes for October 2024

Secure SDLC: Incorporating Blockchain for Enhanced Security

In the rapidly evolving landscape of software development, security has emerged as a critical concern, particularly as the frequency and sophistication of cyber threats continue to rise. The Software Development Life Cycle (SDLC) traditionally emphasizes security at various stages; however, the integration of cutting-edge technologies such as blockchain has the potential to revolutionize this process. This research explores the incorporation of blockchain technology into the Secure SDLC to enhance security measures throughout the software development process. Blockchain, characterized by its decentralized, transparent, and immutable nature, offers a robust framework for mitigating risks associated with software vulnerabilities, data breaches, and unauthorized access. The study delves into how blockchain can be seamlessly integrated into each phase of the SDLC—requirements analysis, design, implementation, testing, deployment, and maintenance. By embedding blockchain protocols within these stages, the SDLC can achieve a higher level of security assurance. For instance, during the requirements analysis and design phases, smart contracts can be utilized to enforce security policies and validate the integrity of design documents. The implementation phase can benefit from blockchain's version control capabilities, ensuring that code changes are tracked, verified, and secure. During testing and deployment, blockchain can facilitate the creation of an immutable audit trail, recording all test results, configurations, and deployments, thereby preventing tampering and ensuring transparency.

Multilingual code refactoring detection based on deep learning

Refactoring is a critical process of improving the internal structure of the source code without altering its external behavior. Existing deep learning-based refactoring detection relies on commit messages to extract features. However, these commit messages are not trustful enough since some developers do not consistently record refactoring activities. Furthermore, current approaches are designed for a single programming language and lack multilingual refactoring support. To this end, this paper proposes RefT5, a multilingual code refactoring detection approach based on deep learning. Firstly, we select 110 real-world projects with Java and Python programming languages as a corpus to construct the dataset. Secondly, we extract features including commit messages, code changes, and refactoring types from these projects. RefT5 generates edit sequences from code changes and takes refactoring types as labels. Thirdly, we employ CodeT5 and BiLSTM-attention to extract semantic and structural features and generate feature vectors. Finally, the feature vectors are input into a classification layer to detect the refactoring type. The experimental results show that RefT5 obtains 98.05% precision and 97.77% recall. Furthermore, compared with existing approaches, it improves precision by 51.61% and recall by 52.9% on average, demonstrating its effectiveness.

Don’t forget to change these functions! recommending co-changed functions in modern code review

Context:Code review is effective and widely used, yet still time-consuming. Especially, in large-scale software systems, developers may forget to change other related functions that must be changed together (aka. co-changes). This may increase the number of review iterations and reviewing time, thus delaying the code review process. Based on our analysis of 66 projects from five open-source systems, we find that there are 16%–33% of code reviews where at least one function must be co-changed, but was not initially changed. Objectives:This study aims to propose an approach to recommend co-changed functions in the context of modern code review, which could reduce reviewing time and iterations and help developers identify functions that need to be changed together. Methods:We propose CoChangeFinder, a novel method that employs a Graph Neural Network (GNN) to recommend co-changed functions for newly submitted code changes. Then, we conduct a quantitative and qualitative evaluation of CoChangeFinder with 66 studied large-scale open-source software projects. Results:Our evaluation results show that our CoChangeFinder outperforms the state-of-the-art approach, achieving 3.44% to 40.45% for top-k accuracy, 2.00% to 26.07% for Recall@k, and 0.04 to 0.21 for mean average precision better than the baseline approach. In addition, our CoChangeFinder demonstrates the capacity to pinpoint the functions related to logic changes. Conclusion:Our CoChangeFinder outperforms the baseline approach (i.e., TARMAQ) in recommending co-changed functions during the code review process. Based on our findings, CoChangeFinder could help developers save their time and effort, reduce review iterations, and enhance the efficiency of the code review process.

Employment Discipline And the Threat of Dismissal Asregulators of the Labor Market in Kazakhstan (SDG)

Objective: This study investigates the role of unemployment as a mechanism of labor discipline in Kazakhstan. It analyzes the relationships between the official unemployment rate and the labor legislation reforms regulating labor discipline that have occurred since Kazakhstan gained independence. The manuscript outlines the developmental stages of labor legislation concerning labor discipline. Method: The research is based on a comprehensive analysis of statistical data, legislative acts, and judicial practices in Kazakhstan. Data sources include official statistics on unemployment and labor disputes, an analysis of changes in the Labor Code of Kazakhstan, and a review of judicial decisions related to disciplinary sanctions and dismissals. Results: The study reveals that strengthening disciplinary responsibility and expanding the powers of employers have significantly contributed to the decline in unemployment in Kazakhstan. These results support the hypothesis that strict labor discipline and pressure on employees can positively impact the overall labor market. However, this impact is not isolated but part of a broader set of measures. Contribution: This research provides a detailed comparative analysis of the number of labor disputes and the evolution of Kazakhstan's labor discipline legislation. Key observations include the correlation between stringent disciplinary norms and reduced unemployment, the influence of judicial practices on labor discipline, and the necessity of balancing strict discipline with fair treatment to prevent legal disputes. The findings offer valuable insights for optimizing labor policies to support sustainable labor market development in Kazakhstan. These insights align with the goals of SDG 8, which promotes sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all.