Improve Code Quality Research Articles

The Scalable Detection and Resolution of Data Clumps Using a Modular Pipeline with ChatGPT

This paper explores a modular pipeline architecture that integrates ChatGPT, a Large Language Model (LLM), to automate the detection and refactoring of data clumps—a prevalent type of code smell that complicates software maintainability. Data clumps refer to clusters of code that are often repeated and should ideally be refactored to improve code quality. The pipeline leverages ChatGPT’s capabilities to understand context and generate structured outputs, making it suitable for addressing complex software refactoring tasks. Through systematic experimentation, our study not only addresses the research questions outlined but also demonstrates that the pipeline can accurately identify data clumps, particularly excelling in cases that require semantic understanding—where localized clumps are embedded within larger codebases. While the solution significantly enhances the refactoring workflow, facilitating the management of distributed clumps across multiple files, it also presents challenges such as occasional compiler errors and high computational costs. Feedback from developers underscores the usefulness of LLMs in software development but also highlights the essential role of human oversight to correct inaccuracies. These findings demonstrate the pipeline’s potential to enhance software maintainability, offering a scalable and efficient solution for addressing code smells in real-world projects, and contributing to the broader goal of enhancing software maintainability in large-scale projects.

AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions

The software engineering landscape is undergoing a significant transformation with the advent of artificial intelligence (AI). AI technologies are poised to redefine traditional software development practices, offering innovative solutions to long-standing challenges. This paper explores the integration of AI into software engineering processes, aiming to identify its impacts, benefits, and the challenges that accompany this paradigm shift. A comprehensive analysis of current AI applications in software engineering is conducted, supported by case studies and theoretical models. The study examines various phases of software development to assess where AI contributes most effectively. The integration of AI enhances productivity, improves code quality, and accelerates development cycles. Key areas of impact include automated code generation, intelligent debugging, predictive maintenance, and enhanced decision-making processes. AI is revolutionizing software engineering by introducing automation and intelligence into the development lifecycle. Embracing AI-driven tools and methodologies is essential for staying competitive in the evolving technological landscape.

Integrating smart contracts into the modeling paradigm to harness the potential of models

Abstract Despite the increasing interest in blockchain and smart contracts, their inherent complexity has impeded widespread adoption. In order to mitigate this issue, this work introduces , a model-based framework for the development of smart contracts in Solidity that enables the treatment of contracts as models, opening up new possibilities for their enhancement and maintenance. A key benefit of is its ability to impose a development pattern, which contributes to improved code quality and reduced vulnerabilities. The framework’s effectiveness is evaluated through several case studies, showing how model-driven engineering can mitigate contracts inherent complexity and promote better collaboration between developers and domain experts. As this work will demonstrate, when smart contracts are treated as models, a vast array of possibilities unfolds.

Design and Research on Evaluation System of Computer Pro-gramming Code Quality

With the ongoing development of software engineering and programming education, the demand for evaluating programming code quality is increasing. High-quality code not only en-hances software maintainability and performance but also helps developers and learners optimize their coding skills. This paper focuses on the design and implementation of an evaluation system for computer programming code quality, proposing a multi-dimensional evaluation method that includes readability, complexity, efficiency, and security. The system combines static analysis with machine learning to automate code analysis and optimization recommendations, providing users with objective feedback on code quality. Additionally, this paper explores application cases of the system in programming education and software development, demonstrating its effectiveness in improving code quality and enhancing users' programming skills. Finally, the research contribu-tions are summarized, and directions for future improvements are proposed to further enhance the system’s intelligence and applicability.

The Role of Artificial Intelligence in Modern Software Engineering

Abstract. The rapid advancement of Artificial Intelligence (AI) has significantly influenced various industries, including software engineering. This paper explores the integration of AI into software engineering, focusing on its applications across different stages of the software development life cycle, including design, development, testing, project management, and maintenance. AI's ability to automate tasks, enhance efficiency, and improve code quality is revolutionizing how software is built and maintained. The paper also addresses the challenges and risks associated with AI-driven software engineering, such as dependency on AI tools, ethical concerns, and security vulnerabilities. Finally, the paper highlights future trends in AI-powered software engineering, including adaptive and self-healing systems, AI-enhanced collaboration, and full software automation. The role of AI in shaping the future of software engineering is both profound and transformative, making it a critical area of study.

FHIR-Based Arden Syntax Compiler for Clinical Decision Support.

The Arden Syntax is a language designed for the encoding of medical knowledge into clinical decision support systems. Its evolution is overseen by Health Level 7. A significant enhancement in its new version 3.0 is the incorporation of FHIR for data retrieval, which addresses the long-standing curly braces problem. We introduce a newly developed compiler for Arden Syntax 3.0, which employs modern tools such as ANTLR for lexical and parsing analysis and GraalVM with the Truffle Language Implementation Framework for semantic processing, optimization, and language execution. Comprehensive testing against a legacy compiler revealed substantial improvements in execution speed, memory efficiency, and code quality. These advancements, coupled with superior maintainability and extensibility, position the Truffle compiler as a robust replacement, supporting future development and enhancing the user experience with Arden-Syntax-based clinical decision support.

Transforming Software Development Through Generative AI : A Systematic Analysis of Automated Development Practices

This article presents a comprehensive analysis of the transformative impact of Generative Artificial Intelligence (GenAI) on software development practices. Through systematic evaluation of implementations across multiple development environments, this article demonstrates how GenAI technologies significantly enhance four critical areas of the software development lifecycle: code generation, automated testing, intelligent code review, and predictive failure analysis. The findings indicate a 45% reduction in initial code development time through automated code generation, 60% improvement in test coverage through AI-driven test case creation, and 30% decrease in post-deployment issues through predictive failure analysis. The article employs a mixed-methods approach, combining quantitative analysis of development metrics from 50 software projects with qualitative assessments from 200 professional developers across diverse organizational contexts. Results reveal that organizations implementing GenAI-assisted development workflows experience a mean productivity increase of 37% while maintaining or improving code quality metrics. Additionally, the article identifies key integration challenges and provides a framework for effective GenAI adoption in software development environments. These findings contribute to the growing body of knowledge on AI-assisted software development and offer practical insights for organizations seeking to optimize their development processes through GenAI integration.

AI Copilot for the Modern Developer : Leveraging GenAI in Software Development

This paper explores the transformative impact of AI copilots on software development, powered by Generative AI and Large Language Models. It examines how these tools enhance developer productivity, improve code quality, and democratize software development. The study delves into the core technologies behind AI copilots, including code synthesis and contextual learning, and discusses their integration with development environments. The paper also investigates the role of reinforcement learning in continuously improving AI copilots and analyzes their impact on software development practices. Additionally, it explores future prospects, including more sophisticated natural language understanding and improved cross-language support. Throughout, the paper emphasizes how AI copilots are reshaping the landscape of software engineering, potentially leading to more efficient, higher-quality, and innovative software solutions.

Enhancing software engineering practices with generative AI: A framework for automated code synthesis and refactoring

This paper is based on how software development has been revolutionized using AI in automation, mainly dealing with code synthesis and rewrite frameworks. While there is no focused definition for software development with AI technologies, their application in development processes is unlikely to remain marginal as they mature and provide higher productivity, improved code quality, and enhanced ability for automating repetitive tasks in development. Automating coding means that predefined tools can bring code suggestions, show and apply refactoring features, and enforce coding standards so developers can devote their efforts to other aspects of software development. The following paper describes the practical systems that should be adopted before AI can be incorporated into software development. It also covers essential factors such as ethical, security, and quality aspects regarding the proper use of artificial intelligence. Lastly, the paper focuses on the direction and innovations in the future, including domain-specific AI models, better explainability of the AI solutions, and collaborative tools that can work according to improved and modified development practices. The framework is intended to balance machine learning with human experience to help developers utilize the benefits that an AI-Based Software Development Framework can render.

The Origin and Opportunities of Developers’ Perceived Code Accountability in Open Source AI Software Development

Open source (OS) software projects in artificial intelligence (AI), such as TensorFlow and scikit-learn, depend on developers' continuous, voluntary code contributions. However, recent security incidents highlighted substantial risks in such software, requiring examinations of factors motivating developers to continuously contribute high-quality code (i.e., providing secure and reliable code fulfilling its functions). Prior research suggests code accountability (i.e., requirements to explain and justify contributed code) to improve code quality, enforced through external accountability mechanisms such as sanctions and rewards. However, the OS domain often lacks such mechanisms, questioning whether and how code accountability arises in this domain and how it affects code contributions. To address these questions, we conducted 26 semi-structured interviews with developers contributing to OS AI software projects. Our findings reveal that despite the absence of external accountability mechanisms, system-, project-, and individual-related factors evoke developers' perceived code accountability. Notably, we discovered a trade-off as high perceived code accountability is associated with higher code quality but discourages developers from participating in OS AI software projects. Overall, this study contributes to understanding the nuanced roles of perceived code accountability in continuously contributing high-quality code without external accountability mechanisms and highlights the complex trade-offs developers face in OS AI software projects.

MORCoRA: Multi-Objective Refactoring Recommendation Considering Review Availability

Background: Search-based refactoring involves searching for a sequence of refactorings to achieve specific objectives. Although a typical objective is improving code quality, a different perspective is also required; the searched sequence must undergo review before being applied and may not be applied if the review fails or is postponed due to no proper reviewers. Aim: Therefore, it is essential to ensure that the searched sequence of refactorings can be reviewed promptly by reviewers who meet two criteria: (1) having enough expertise and (2) being free of heavy workload. The two criteria are regarded as the review availability of the refactoring sequence. Method: We propose MORCoRA, a multi-objective search-based technique that can search for code quality improvable, semantic preserved, and high review availability possessed refactoring sequences and corresponding proper reviewers. Results: We evaluate MORCoRA on six open-source repositories. The quantitative analysis reveals that MORCoRA can effectively recommend refactoring sequences that fit the requirements. The qualitative analysis demonstrates that the refactorings recommended by MORCoRA can enhance code quality and effectively address code smells. Furthermore, the recommended reviewers for those refactorings possess high expertise and are available to review. Conclusions: We recommend that refactoring recommenders consider both the impact on quality improvement and the developer resources required for review when recommending refactorings.

Advanced test automation techniques for DevOps: Bridging the gap between test-driven development and continuous deployment in agile environments

As software development continues to change, it becomes more important to incorporate modern test automation approaches in DevOps to support efficient deployment. This work investigates TDD and CD in Agile to critically appraise advanced automation technologies and practices to fill existing deficits. This research demonstrates that the brighter Test Automation Framework approaches could improve code quality and deployment time, along with the culture of embracing best practices. The results can help practitioners who want to make their DevOps practices even more efficient and focused on quickly delivering high-quality products.

Enhancing Program Synthesis with Large Language Models Using Many-Objective Grammar-Guided Genetic Programming

The ability to automatically generate code, i.e., program synthesis, is one of the most important applications of artificial intelligence (AI). Currently, two AI techniques are leading the way: large language models (LLMs) and genetic programming (GP) methods—each with its strengths and weaknesses. While LLMs have shown success in program synthesis from a task description, they often struggle to generate the correct code due to ambiguity in task specifications, complex programming syntax, and lack of reliability in the generated code. Furthermore, their generative nature limits their ability to fix erroneous code with iterative LLM prompting. Grammar-guided genetic programming (G3P, i.e., one of the top GP methods) has been shown capable of evolving programs that fit a defined Backus–Naur-form (BNF) grammar based on a set of input/output tests that help guide the search process while ensuring that the generated code does not include calls to untrustworthy libraries or poorly structured snippets. However, G3P still faces issues generating code for complex tasks. A recent study attempting to combine both approaches (G3P and LLMs) by seeding an LLM-generated program into the initial population of the G3P has shown promising results. However, the approach rapidly loses the seeded information over the evolutionary process, which hinders its performance. In this work, we propose combining an LLM (specifically ChatGPT) with a many-objective G3P (MaOG3P) framework in two parts: (i) provide the LLM-generated code as a seed to the evolutionary process following a grammar-mapping phase that creates an avenue for program evolution and error correction; and (ii) leverage many-objective similarity measures towards the LLM-generated code to guide the search process throughout the evolution. The idea behind using the similarity measures is that the LLM-generated code is likely to be close to the correct fitting code. Our approach compels any generated program to adhere to the BNF grammar, ultimately mitigating security risks and improving code quality. Experiments on a well-known and widely used program synthesis dataset show that our approach successfully improves the synthesis of grammar-fitting code for several tasks.

The Role of Machine Learning in Software Development

In today's rapidly evolving technological era, the role of machine learning in software development has become increasingly vital and influential. Machine learning has revolutionized various aspects of software development, from code analysis and optimization to prediction and decision-making. Moreover, machine learning algorithms have the potential to significantly enhance the software development process by automating repetitive tasks, improving code quality, and reducing the time and effort required for software testing and debugging. By gaining lots of data and powerful computing resources, machine learning algorithms can be able to analyse patterns and make accurate predictions about software performance, identify potential bugs or security issues, and assist in improving software design and development processes. Furthermore, machine learning can facilitate software maintenance and debugging by detecting anomalies and identifying potential causes of software failures. Albeit, using machine learning techniques into the software development process can greatly improve efficiency, productivity, and overall software quality. In this paper it is presenting the tools, techniques and the application of Machine Learning (ML) in different phases of Software Development Life Cycle (SDLC) for enhancing and improving the software development process.

Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues

Since its introduction in November 2022, ChatGPT has rapidly gained popularity due to its remarkable ability in language understanding and human-like responses. ChatGPT, based on GPT-3.5 architecture, has shown great promise for revolutionizing various research fields, including code generation. However, the reliability and quality of code generated by ChatGPT remain unexplored, raising concerns about potential risks associated with the widespread use of ChatGPT-driven code generation. In this article, we systematically study the quality of 4,066 ChatGPT-generated programs of code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal of this work is threefold. First, we analyze the correctness of ChatGPT on code generation tasks and uncover the factors that influence its effectiveness, including task difficulty, programming language, time that tasks are introduced, and program size. Second, we identify and characterize potential issues with the quality of ChatGPT-generated code. Last, we provide insights into how these issues can be mitigated. Experiments highlight that out of 4,066 programs generated by ChatGPT, 2,756 programs are deemed correct, 1,082 programs provide wrong outputs, and 177 programs contain compilation or runtime errors. Additionally, we further analyze other characteristics of the generated code through static analysis tools, such as code style and maintainability, and find that 1,930 ChatGPT-generated code snippets suffer from maintainability issues. Subsequently, we investigate ChatGPT’s self-repairing ability and its interaction with static analysis tools to fix the errors uncovered in the previous step. Experiments suggest that ChatGPT can partially address these challenges, improving code quality by more than 20%, but there are still limitations and opportunities for improvement. Overall, our study provides valuable insights into the current limitations of ChatGPT and offers a roadmap for future research and development efforts to enhance the code generation capabilities of artificial intelligence models such as ChatGPT.

Testability-driven development: An improvement to the TDD efficiency

Test-first development (TFD) is a software development approach involving automated tests before writing the actual code. TFD offers many benefits, such as improving code quality, reducing debugging time, and enabling easier refactoring. However, TFD also poses challenges and limitations, requiring more effort and time to write and maintain test cases, especially for large and complex projects. Refactoring for testability is improving the internal structure of source code to make it easier to test. Refactoring for testability can reduce the cost and complexity of software testing and speed up the test-first life cycle. However, measuring testability is a vital step before refactoring for testability, as it provides a baseline for evaluating the current state of the software and identifying the areas that need improvement. This paper proposes a mathematical model for calculating class testability based on test effectiveness and effort and a machine-learning regression model that predicts testability using source code metrics. It also introduces a testability-driven development (TsDD) method that conducts the TFD process toward developing testable code. TsDD focuses on improving testability and reducing testing costs by measuring testability frequently and refactoring to increase testability without running the program. Our testability prediction model has a mean squared error of 0.0311 and an R2 score of 0.6285. We illustrate the usefulness of TsDD by applying it to 50 Java classes from three open-source projects. TsDD achieves an average of 77.81 % improvement in the testability of these classes. Experts’ manual evaluation confirms the potential of TsDD in accelerating the TDD process.

Automating modern code review processes with code similarity measurement

Context:Modern code review is a critical component in software development processes, as it ensures security, detects errors early and improves code quality. However, manual reviews can be time-consuming and unreliable. Automated code review can address these issues. Although deep-learning methods have been used to recommend code review comments, they are expensive to train and employ. Instead, information retrieval (IR)-based methods for automatic code review are showing promising results in efficiency, effectiveness, and flexibility. Objective:Our main objective is to determine the optimal combination of the vectorization method and similarity to measure what gives the best results in an automatic code review, thereby improving the performance of IR-based methods. Method:Specifically, we investigate different vectorization methods (Word2Vec, Doc2Vec, Code2Vec, and Transformer) that differ from previous research (TF-IDF and Bag-of-Words), and similarity measures (Cosine, Euclidean, and Manhattan) to capture the semantic similarities between code texts. We evaluate the performance of these methods using standard metrics, such as Blue, Meteor, and Rouge-L, and include the run-time of the models in our results. Results:Our results demonstrate that the Transformer model outperforms the state-of-the-art method in all standard metrics and similarity measurements, achieving a 19.1% improvement in providing exact matches and a 6.2% improvement in recommending reviews closer to human reviews. Conclusion:Our findings suggest that the Transformer model is a highly effective and efficient approach for recommending code review comments that closely resemble those written by humans, providing valuable insight for developing more efficient and effective automated code review systems.

ДОСЛІДЖЕННЯ ОСОБЛИВОСТЕЙ ЗАСТОСУВАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В ІНЖЕНЕРІЇ ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ

The study explores the transformative impact of artificial intelligence in the realm of software development, showcasing how AI-powered tools streamline workflows and enhance productivity. From automating documentation creation with tools like Rewind.ai and Mutable.ai to translating code into human language for easier comprehension through Figstack, AI is revolutionizing development practices. These innovations underscore AI's pivotal role in reshaping software development processes, improving code quality, and fostering efficient teamwork in an increasingly dynamic landscape.

Strategies for implementing or strengthening the DevOps approach in organizations: Analysis and examples

The aim of the study was to analyse the implementation of DevOps in organizations, in particular, to assess the impact of process automation, CI/CD, monitoring and orchestration of microservices on the efficiency of development and management. A methodology was created that allows organizations to effectively implement and enhance the DevOps approach, achieving high results in software development and management. The study looked at cultural change and training strategies, including building a culture of collaboration between teams and developing skills in modern DevOps tools. Process automation, including test automation and integration and deployment, plays an important role in improving code quality and reducing development time. Infrastructure as code allows managing and automating infrastructure configuration, enabling rapid scaling of environments. The work has shown that performance monitoring and feedback are critical to detecting problems early and continuously improving the product. Microservices orchestration, in particular, with Kubernetes, automates the deployment and scaling of containerized applications, which is critical for modern architectures with high performance and availability requirements. Case studies of companies such as Netflix, Spotify, and Airbnb demonstrate the successful application of DevOps practices and technologies to achieve high availability, scalability, and reliability of infrastructures. The study results confirm that the implementation of DevOps leads to a significant increase in development efficiency, software quality, and optimization of infrastructure management costs. In addition, the introduction of cultural changes and increased collaboration between development and operations teams contributes to greater flexibility and speed of response to changing market conditions

APPLICATION OF ARTIFICIAL INTELLIGENCE IN PROGRAMMING EDUCATION WITHIN A BLENDED LEARNING ENVIRONMENT

Blended learning, which combines traditional teaching methods with online tools, requires effective technological solutions, particularly for ensuring continuous feedback between teachers and students. This article analyzes the current state of organizing blended learning and explores the use of modern information technologies, specifically artificial intelligence and neural networks, in programming education. The authors present a detailed description of the educational platform VirtualLaboratories, a concept for a programming education platform that leverages artificial intelligence, particularly neural networks, to automate various aspects of coding, assessment, and evaluation of completed programming solutions. The platform supports multiple programming languages, such as C#, SQL, and Python, and integrates leading neural networks for code analysis, optimization, and quality improvement. This includes models like GPT-4 Code, CodeBERT, and CodeT5, which provide not only automatic code analysis but also recommendations for improvement and optimization. These capabilities allow teachers to focus on individualized work with students by tracking their progress, while students can tackle assignments at their convenience and receive objective, high-precision analyses of their solutions. The article also provides examples of neural network integration for automating code evaluation processes, which significantly reduces the time required for reviewing work and minimizes errors. It describes the main functional features of the platform, which enable the creation of multi-level programming tasks, real-time feedback, and error analysis to improve programming solutions. Additionally, the article outlines prospects for further research on implementing new artificial intelligence algorithms and expanding the platform’s capabilities to support more complex tasks and programming languages.