Identifying nonconformities in contributions to programming projects: from an engagement perspective in improving code quality

Block-based programming has been overwhelmingly successful in revitalizing introductory computing education and in facilitating end-user development. However, poor code quality makes block-based programs hard to understand, modify, and reuse, thus hurting the educational and productivity effectiveness of blocks. There is great potential benefit in empowering programmers in this domain to systematically improve the code quality of their projects. Refactoring--improving code quality while preserving its semantics--has been widely adopted in traditional software development. In this work, we introduce refactoring to Scratch. We define four new Scratch refactorings: Extract Custom Block, Extract Parent Sprite, Extract Constant, and Reduce Variable Scope. To automate the application of these refactorings, we enhance the Scratch programming environment with powerful program analysis and transformation routines. To evaluate the utility of these refactorings, we apply them to remove the code smells detected in a representative dataset of 448 Scratch projects. We also conduct a between-subjects user study with 24 participants to assess how our refactoring tools impact programmers. Our results show that refactoring improves the subjects' code quality metrics, while our refactoring tools help motivate programmers to improve code quality.

To sustain the effort of work team members as a collaborative resource over time, organizations need to avoid the social loafing of employees. Recognizing the importance of this relationship, we investigated both individual (i.e., exchange ideology) and situational factors (i.e., task visibility and professional respect) that influence employees’ attitudes toward social exchange and team collaboration and thus their social loafing. Using survey responses of 223 employee–supervisor dyads in South Korean firms, hierarchical regression analyses demonstrated that employees with a low exchange ideology were unlikely to engage in social loafing. In addition, exchange ideology interacted with both task visibility and professional respect to influence social loafing. Specifically, task visibility was negatively associated with social loafing, and this effect was stronger when employees were low, rather than high, in exchange ideology. Moreover, professional respect was negatively associated with social loafing, and this effect was stronger among those low in exchange ideology. Thus, a low level of exchange ideology can reduce social loafing and can also enhance the potential for task visibility and professional respect to strengthen effort levels. Accordingly, this study suggested that individual characteristics, especially exchange ideology, are important in reducing social loafing and clearly stated what managerial guidelines can be implemented to practically reduce social loafing.

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.

Purpose This study aimed to evaluate the effectiveness of a domain-specific hybrid teaching model for the undergraduate Electric and Hybrid Vehicles course. The model integrates traditional lectures, flipped classroom strategies, and project-based learning to improve student engagement, learning outcomes, and practical application skills. It also examines the potential of AI-based predictive analytics in monitoring and enhancing student performance. The broader purpose is to align teaching methods with NBA and ABET accreditation standards and explore opportunities for scalable implementation in engineering education. Design/methodology/approach A quasi-experimental mixed-methods design was used. The experimental group (n = 30) was taught using the hybrid model comprising traditional lectures (20%), flipped classroom strategies (50%), and project-based learning (30%). The control group (n = 30) received conventional instruction. Pre- and post-test scores measured knowledge gain, while project outcomes assessed practical learning. Student engagement was predicted using an AI-driven decision tree classifier trained on Moodle activity data. Quantitative analysis was supported with qualitative feedback to assess satisfaction levels and perceived learning improvements. Findings The experimental group demonstrated a 41.67% improvement in post-test scores compared to a 20.69% gain for the control group, with large effect sizes indicating strong impact. The AI-driven classifier achieved 87% accuracy in predicting student engagement, enabling targeted interventions for at-risk learners. Student satisfaction levels were high at 88%, and project outcomes showed marked improvement in creativity, technical quality, and teamwork. These findings confirm the hybrid model's effectiveness in enhancing conceptual understanding, practical application, and learner engagement beyond what conventional teaching methods deliver. Research limitations/implications The study was conducted with a relatively small sample size (n = 60) from a single institution, which may limit generalizability. The focus was restricted to one specialized course, so further research is required to validate the model across multiple domains and disciplines. Long-term retention of knowledge and transferability to professional contexts were not measured. Future studies could expand the sample, apply advanced AI methods for predictive analytics, and investigate scalability in diverse institutional settings. These findings imply the potential for hybrid pedagogy and AI-based learning analytics in engineering education. Practical implications The hybrid model provides engineering educators with a structured, evidence-based teaching approach that enhances both theoretical knowledge and applied skills. The integration of AI-driven engagement monitoring enables proactive support for students, reducing dropout risks and improving academic outcomes. Institutions can adopt this framework to align with NBA and ABET accreditation requirements while improving student readiness for industry challenges. The approach is scalable, making it feasible for wider implementation across technical courses. Project-based components foster innovation, teamwork, and problem-solving skills, directly benefiting professional preparedness. Social implications By enhancing engineering education quality C, the hybrid model contributes To Producing More industry-ready graduates who are skilled in emerging areas like Electric and Hybrid Vehicles. The integration of AI for learning analytics demonstrates how technology can personalize education and support equity by identifying and assisting struggling learners early. Improved student satisfaction and engagement can foster a culture of lifelong learning. Broader adoption of such models can strengthen the technical workforce C contributing to sustainable mobility solutions and addressing global challenges in clean energy and transportation. Originality/value This study is among the first to design and test a domain-specific hybrid teaching model in Electric and Hybrid Vehicle education, combining flipped classroom, project-based learning, and lectures in a structured ratio. The integration of AI-driven predictive analytics adds a novel dimension by enabling personalized interventions based on student engagement data. The research provides empirical evidence of significant learning gains, improved project outcomes, and alignment with international accreditation standards. Its value lies in demonstrating a scalable, replicable, and innovative framework for modern engineering education.

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.

Situational engagement captures students’ momentary experiences during learning activities and can provide insights that are not accessible through more general measures of engagement. This study examined how a robotics and coding project-based learning (PBL) module influenced fourth-grade students’ situational engagement, focusing on variations across four instructional phases – skill building, ideation, skill implementation and co-reflection – and identifying latent engagement profiles. Using an Experience Sampling Method (ESM) in a repeated-measures design, 143 students reported their perceived skill, interest, and challenge levels at 12 measurement points throughout the project. As situational engagement may not be uniform across an entire class, employing Latent Profile Analysis (LPA) helps identify subgroups of students who share distinct engagement patterns. LPA identified four engagement profiles – engaged , underchallenged , moderately engaged , and disengaged – and revealed consistently high levels of situational engagement, with notable variations in challenge and interest experiences across phases. Engagement peaks occurred during skill-building activities (Optimal Learning Moment, OLM rate = 26%), whereas the ideation phase produced the lowest OLM rate (20%) alongside the highest perceived challenge. These findings highlight the value of balancing structured and unstructured phases in PBL and provide guidance for educators on how to sustain students’ engagement throughout the module, particularly in mathematics and science contexts.

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.

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.

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.

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.

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.

DevOps is the new approach in software development that has encouraged interaction between the development and operation teams. DevOps also involves using feedback mechanisms that enhance continuous and rapid cycle feedback. However, what often occurs is that these feedback loops must be managed manually, which takes time and can be prone to mistakes. This paper explores AI and ML's ability to perform feedback loops in DevOps pipelines. In the context of the current study, we investigate the application of big data and real-time monitoring to improve code quality problem detection and prediction of performance consequences. We illustrate how feedback to developers about the necessary changes can be provided through ML models to analyze data obtained from different sources like application logs, monitoring tools, and user interactions. The paper explains the basics needed to establish feedback loops based on ML, which include data acquisition, data cleaning, model training, and online prediction. We also discuss the issues and concerns when using AI/ML in DevOps, such as the model's interpretability, the tool's integration, and change management. In this article, we explain how AI can make feedback loops smarter and provide case studies and real-life examples of how this can help improve code quality, solve problems more quickly, and enhance the relationship between development and operations. Last but not least, we discuss potential research directions for this area's further development, such as approaches to improve model interpretability, integrating collaborative learning into the DevOps process, and creating reference models for AI adoption in DevOps pipelines. In this paper, we use AI and ML to map out ways for organizations to improve their DevOps processes and ensure a feedback loop at every stage.

This chapter covers using Faces 4.0’s built-in templating engine, called Facelets. Compared to using plain Faces, you can use Facelets to further modularize your frontend code and improve code quality, reuse, and maintenance.

This paper describes our trace-based JIT compiler (trace-JIT) for Java developed from a production-quality method-based JIT compiler (method-JIT). We first describe the design and implementation of our trace-JIT with emphasis on how we retrofitted a method-JIT as a trace-based compiler. Then we show that the trace-JIT often produces better quality code than the method-JIT by extending the compilation scope. Forming longer traces that span multiple methods turns out to be more powerful than method inlining in extending the compilation scope. It reduces method-invocation overhead and also offers more compiler optimization opportunities. However, the trace-JIT incurs additional runtime overhead compared to the method-JIT that may be offset by gains from the improved code quality. Overall, our trace-JIT achieved performance roughly comparable to the baseline method-JIT. We also discuss the issues in trace-based compilation from the viewpoint of compiler optimizations. Our results show the potentials of trace-based compilation as an alternative or complementary approach to compiling languages with mature method-based compilers.