Code Changes Research Articles

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.

Risk assessment for 3D printing in construction projects

Purpose The purpose of this study is to identify and assess new risks in construction projects that use 3D printing. Design/methodology/approach A mixed approach of both qualitative and quantitative methods was used. Literature review was conducted to extract 30 risks of 3D printing in construction. A survey was then developed to assess the probability and impact of these risks. In total, 37 respondents, who have experience and/or knowledge of 3D printing, completed the survey. The risk priority was calculated using a fuzzy logic approach. The main benefit of the proposed model is being able to use numerical and linguistic data in the risk assessment model. Findings The results show that the main risks, in terms of priority, are lack of codes and regulations for 3D printing in construction, delay in government approvals, shortage in labour skilled in 3D printed construction, lack of knowledge and information of 3D printed design concepts and changes in 3D construction codes and regulations. Originality/value This paper fills an identified gap in the literature related to 3D printing in construction and provides insights into the key risks affecting this disruptive technology.

Reproduce to validate: A comprehensive study on the reproducibility of economics research

AbstractJournals have pushed for transparency of research through data availability policies. Such data policies improve availability of data and code, but what is the impact on reproducibility? We present results from a large reproduction exercise for articles published in the American Economic Journal: Applied Economics, which has had a data availability policy since its inception in 2009. Out of 363 published articles, we assessed 274 articles. All articles provided some materials. We excluded 122 articles that required confidential or proprietary data or that required the replicator to otherwise obtain the data (44.5% of assessed articles). We attempted to reproduce 152 articles and were able to fully reproduce the results of 68 (44.7% of attempted reproductions). A further 66 (43.4% of attempted reproductions) were partially reproduced. Many articles required complex code changes even when at least partially reproduced. We collect bibliometric characteristics of authors, but find no evidence for author characteristics as determinants of reproducibility. There does not appear to be a citation bonus for reproducibility. The data availability policy of this journal was effective to ensure availability of materials, but is insufficient to ensure reproduction without additional work by replicators.

MULTICR: Predicting Merged and Abandoned Code Changes in Modern Code Review Using Multi-Objective Search

Modern Code Review (MCR) is an essential process in software development to ensure high-quality code. However, developers often spend considerable time reviewing code changes before being merged into the main code base. Previous studies attempted to predict whether a code change was going to be merged or abandoned soon after it was submitted to improve the code review process. However, these approaches require complex cost-sensitive learning, which makes their adoption challenging since it is difficult for developers to understand the main factors behind the models’ predictions. To address this issue, we introduce in this paper, MULTICR, a multi-objective search-based approach that uses Multi-Objective Genetic Programming (MOGP) to learn early code review prediction models as IF-THEN rules. MULTICR evolves predictive models while maximizing the accuracy of both merged and abandoned classes, eliminating the need for misclassification cost estimation. To evaluate MULTICR, we conducted an empirical study on 146,612 code reviews from Eclipse, LibreOffice, and Gerrithub. The obtained results show that MULTICR outperforms existing baselines in terms of Mathew correlation coefficient (MCC) and F1 scores while learning less complex models compared to decision trees. Our experiments showed also how MULTICR allows identifying the main factors related to abandoned code reviews as well as their associated thresholds, making it a promising approach for early code review prediction with notable performance and interoperability. Additionally, we qualitatively evaluate MULTICR by conducting a user study through semi-structured interviews involving 10 practitioners from different organizations. The obtained results indicate that 90% of the participants find that MULTICR is useful and can help them to improve the code review process. Additionally, the learned IF-THEN rules of MULTICR are transparent.

Automated patch correctness predicting to fix software defect

Automated Program Repair (APR) is a technique that can automatically fix software defects without manual debugging, playing a crucial role in software development and maintenance. However, the patches generated by APR still suffer from the problem of overfitting, which poses a significant threat to practical applications. Previous studies have proposed various approaches to predict the correctness of the patch to address this issue, primarily including static-based methods, dynamic-based methods, and learning-based methods. However, these methods all have their own limitations, making it difficult to accurately extract code semantic features and achieve comprehensive prediction. To address the aforementioned challenges, we propose a learning-based unsupervised classification model, Automated Patch cOrrectness aSsessmenT based on muLtiple pErspectives (APOSTLE), for predicting the correctness of patches. Specifically, APOSTLE consists of three components: code vectorization component, where advanced pre-trained models are used to achieve efficient extraction of semantic features from the code; similarity and code change degree calculation component, where APOSTLE calculates the similarity and the degree of change to the code; comprehensive evaluation component, where APOSTLE conducts comprehensive evaluation, addressing the issue of prediction comprehensiveness. Experiments on a collection of 1278 patches (written by developers or generated by 32 APR tools) demonstrate that APOSTLE achieves an AUC value of 0.801, an MAP value of 0.855, and an MRR value of 0.944, outperforming the state-of-the-art approach BATS by 8.3%, 6.0%, and 9.0%, respectively. APOSTLE successfully achieves accurate extraction of code semantic features while achieving comprehensive patch correctness prediction.