Code Quality Improvement for All: Automated Refactoring for Scratch

Early prediction of quality of service using interface-level metrics, code-level metrics, and antipatterns

Achieving comprehensive code quality and ensuring software reliability are critical goals in modern software engineering. This paper delves into advanced strategies that encompass both technical and organizational practices, aiming to enhance code quality and boost software reliability. Key strategies include the implementation of rigorous code review processes, adoption of automated testing frameworks, and the utilization of static and dynamic code analysis tools. Firstly, rigorous code review processes are fundamental for maintaining high standards of code quality. By fostering a collaborative environment where peer reviews are routine, teams can catch potential issues early, ensure adherence to coding standards, and promote knowledge sharing. This practice not only improves code quality but also enhances team cohesion and expertise. Secondly, automated testing frameworks are indispensable in achieving comprehensive test coverage and reducing the incidence of bugs. Unit tests, integration tests, and end-to-end tests, when automated, provide continuous feedback and ensure that code changes do not introduce new defects. This continuous testing approach is crucial for maintaining software reliability, especially in agile and DevOps environments where rapid iterations are common. Furthermore, the integration of static and dynamic code analysis tools into the development pipeline offers additional layers of quality assurance. Static analysis tools inspect the codebase for potential vulnerabilities, code smells, and compliance with coding standards without executing the program. Dynamic analysis tools, on the other hand, evaluate the software during runtime to detect memory leaks, concurrency issues, and performance bottlenecks. Together, these tools provide a comprehensive assessment of the code’s health and reliability. Moreover, fostering a culture of continuous improvement and learning within the development team is essential. Regular training sessions, workshops, and knowledge-sharing activities help in keeping the team updated with the latest advancements in software engineering practices and tools. Encouraging a mindset of quality-first and reliability among developers ensures long-term benefits and sustainability. In conclusion, achieving comprehensive code quality and ensuring software reliability require a multifaceted approach that integrates rigorous code reviews, automated testing frameworks, and both static and dynamic analysis tools. Coupled with a strong culture of continuous learning and improvement, these strategies collectively contribute to the development of robust, reliable, and high-quality software systems. Keywords: Software Reliability, Code Quality, Advanced, Strategies, Comprehensive.

Maintenance is one of the most effort consuming activities in the software development lifecycle. Efficient maintenance within short release cycles depends highly on the underlying source code structure, in the sense that complex modules are more difficult to maintain. In this paper we attempt to unveil and discuss relationships between maintenance productivity, the structural quality of the source code and process metrics like the type of a release and the number of downloads. To achieve this goal, we developed a Bayesian Belief Network (BBN) involving several maintainability predictors and three managerial indices for maintenance (i.e., duration, production, and productivity) on 20 open source software projects. The results suggest that maintenance duration depends on inheritance, coupling, and process metrics. On the other hand maintenance production and productivity depend mostly on code quality metrics.

The nature of HPC application development encourages ad hoc design and implementation, rather than formal requirements analysis and design specification as is typical in software engineering. However, we cannot simply expect HPC developers to adopt formal software engineering processes wholesale, even while there is a need to improve software structure and quality to ensure future maintainability. Therefore, we propose tools that HPC developers can use at their discretion to obtain feedback on the structure and quality of their codes. This feedback would come in the form of code quality metrics and analyses, presented when necessary in intuitive and interactive visualizations. This paper summarizes our implementation of just such a tool, which we apply to a standard HPC benchmark as proofof- concept.

Pair programming, a collaborative coding practice, has been widely adopted in software development, but its application in Data Engineering is still emerging.We will review the benefits of pair programming in Data Engineering, particularly with the integration of Generative AI (Gen-AI) tools.We discuss how pair programming enhances code quality, reduces errors, and improves knowledge sharing among data engineers.Furthermore, we examine how Gen-AI assistants, like Meta-AI, Github co-pilot, and Google's Gemini in Colab, can augment pair programming by providing real-time suggestions, automating routine tasks, and facilitating more efficient problem-solving.Our review reveals several advantages of combining pair programming with Gen-AI in Data Engineering, such as improved code quality, enhanced collaboration, and more.

Project-based learning is among the most common learning approaches aimed at conveying professional standards and best practices to students in programming courses. However, team projects commonly impose problems related to responsibility sharing, such as low effort or inequality in contributions. This paper presents a collaborative programming assessment system featuring a code quality assessment function with specific metrics to measure individual contributions. Student engagement data is used to detect nonconformities in collaboration using a learning analytical approach. Latent profile analysis was used to detect four theoretical team profiles differentiated by team effort (2 levels) and within-team collaboration (2 levels). We demonstrated the efficacy of assessing code to evaluate team dynamics and student behaviour, wherein efforts to resolve coding style failures can be used as a proxy by which to estimate the taskwork awareness of team members. Submission data from 146 students in 41 web-programming projects revealed four behavioural patterns that could potentially hinder the effective functioning of programming teams: free-riding, social loafing, the bystander effect, and lone wolves. We also demonstrated the applicability of automated programming assessment systems to the monitoring of learning progress, thereby facilitating timely interventions to correct difficulties at the team level.

Technical debt is a metaphor that refers to all the consequences of poorly written code and trade-offs in development. Early technical debt diagnosis is important for software developers because it allows planning for software maintenance and improvement activities, such as refactoring, to prevent system degradation. Several studies have been conducted in the literature on the identification of the technical debt and its consequences, thanks to useful tools for identifying the problem within the source code. On the other hand, this work aims to explore a deep learning approach to predict the rise of technical debt in software code by leveraging the knowledge of changing quality metrics. For validation of the approach, a large dataset was built, related to four known Java software projects, with the collection of numerous class-level code quality metrics. The results obtained show the effectiveness of the proposed approach in predicting the development of Technical Debt within the source code. We obtained an F1 score of 0.99 for two of the chosen software systems and greater than 0.91 for the remaining two.

classification is the problem of identifying a set of categories to a new comments.To improve the efficiency of the code,quality metrics are applied for evaluation.The binary classifier,predicts the false positive rates with lesser accuracy,and limited number of classes only to predict the accuracy for classifier.To address this problem,support vector machine classifier is used,which helps in detecting the false positive rates,improving code quality and the accuracy will also increased.

Mutation testing is an effective testing technique to detect faults and improve code quality. However, few practitioners have adopted mutation testing into practice, which raises several questions: Are tests capable of killing mutants useful? What is the main hindrance to adopting mutation testing in practice? Can practitioners really integrate mutation testing into real-world agile development processes? In this paper, we present two major contributions. First, based on our analysis and knowledge of Ruby, we devised eight new mutation operators for Ruby. Second, we applied mutation testing to an industrial Ruby project at Medidata and reported the lessons learned from the study. We confirmed that mutation-adequate tests are useful and could improve code quality from the perspective of practitioners and found long mutation execution time hinders the agile process. In addition, we used an enterprise-level Amazon cloud-computing technique to reduce the computational cost of running mutants. Considering the availability of a mutation testing tool with our suggested features, we argue that mutation testing can be used in practice.

With the rapid development of deep learning, the implementation of intricate algorithms and substantial data processing has become a standard element of deep learning projects. As a result, the code has become progressively complex as the software evolves, which is difficult to maintain and understand. Existing studies have investigated the impact of refactoring on software quality within non-deep learning software. However, the insights of code refactoring in the context of deep learning are still unclear. This study endeavors to fill this knowledge gap by empirically examining the current state of code refactoring in the deep learning realm and practitioners' views on refactoring tools. We first manually analyze the commit history of five popular and well-maintained deep learning projects (e.g., PyTorch). We mine <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4, 4 0 1}$</tex> refactoring practices in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2, 4 4 5}$</tex> historical commits and measure how different types and elements of refactoring operations are distributed. We then survey 159 practitioners about their views of code refactoring in deep learning projects and their expectations of current refactoring tools. The survey result shows that refactoring research and the development of related tools in the field of deep learning are crucial for improving project maintainability and code quality, and that current refactoring tools do not adequately meet the needs of practitioners. Lastly, we provide our perspective on the future advancement of refactoring tools and offer suggestions for developers' development practices.

Nowadays, sites are complex applications that carry out transactions, render real-time information and offer interaction. Creation of web applications that allow many designers, advanced tools, and many more options. Web frameworks deliver a brilliant way between creating an application from the ground and using a content management system. This article centers around an open source web development framework, to be specific to Django. The methodology that used in the study is measuring Django Web Frameworks code quality metrics using Radon and Pylint. Django option in the main directory has 2,200 lines of code, Cyclomatic Complexity score is 16.375 considered as average complexity, and 6.69/10 by the pylint score.

In today’s rapidly changing software development landscape, ensuring code quality is essential to reliability, maintainability, and security among other aspects. Identifying code quality issues can be tackled; however, implementing code quality improvements can be a complex and time‐consuming task. To address this problem, we present a novel methodology designed to assist developers by suggesting alternative code snippets that not only match the functionality of the original code but also improve its quality based on predefined metrics. Our system is based on a language‐agnostic approach that allows the analysis of code snippets written in different programming languages. It employs advanced techniques to assess functional similarity and evaluates syntactic similarity, suggesting alternatives that minimize the need for extensive modification. The evaluation of our system on multiple axes demonstrates the effectiveness of our approach in providing usable code alternatives that are both functionally equivalent and syntactically similar to the original snippets, while significantly improving quality metrics. We argue that our methodology and tool can be valuable for the software engineering community, bridging the gap between the identification of code quality problems and the implementation of practical solutions that improve software quality.

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.

Refactoring has been defined as improving code quality without affecting its functionality. When refactoring is overlooked in daily development, the likelihood of larger refactorings increases with time. Disadvantages of larger refactorings include that they disrupt the daily work, require additional planning effort, and often they need to be justified to stakeholders. In this paper, we investigate with interviews how professionals make refactoring decisions. As a result, we present a framework for decision making for larger refactoring operations describing the key stages in a refactoring workflow. Furthermore, one actual industry case of refactoring decision making is presented in detail.

Code review is a widespread practice in software engineering during which developers examine each other’s source code changes to identify potential issues and improve code quality. Among the automated techniques proposed by researchers to reduce the manual workload of code review, Automated Code Revision (ACR) aims to automatically address reviewers’ feedback by producing a revised version of the code. Transformer-based language models have demonstrated state-of-the-art results in ACR. The performance of these models, however, is significantly influenced by the quality and preparation of the training and evaluation data. We present several systematic analyses of prevalent preprocessing steps, examined both cumulatively and in isolation, across three established preprocessing pipelines and two dataset splitting strategies (time-level vs. project-level). Our study spans across models of different scales: OpenNMT (small), T5 and CodeReviewer (mid-sized), LoRA-tuned CodeLLaMA-7B (large), and GPT-3.5-Turbo (large, black-box). Using datasets up to 496k training records, we evaluate and statistically compare models’ performance using exact match ratio (EXM), CodeBLEU, and Levenshtein ratio. Our findings show that preprocessing may be a significant component in the success of the different techniques: OpenNMT relies on heavy preprocessing; T5 benefits from light filtering (selective removal of records); CodeReviewer performs best when trained on larger, less aggressively filtered data; CodeLLaMA-7B and ChatGPT-3.5 Turbo are largely indifferent to preprocessing. Overall, the effectiveness of ACR tools depends on aligning preprocessing with model scale and training setup. In general, small models need abstraction, mid-sized ones benefit from light filtering, and large-scale models perform best when trained on the original, unprocessed form of the code.