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1. Auglets: Intelligent Tutors for Learning Good Coding Practices by Solving Refactoring Problems

   https://doi.org/10.1145/3649165.3690119  

   Kumar, Amruth N (December 2024, ACM)

   Code quality is of universal concern among educators. Refactoring code, i.e., revising the structure of a program without changing its behavior is one approach for improving code quality. Numerous software tools have been created to help students refactor the code they write. Only a few software tutors have been reported in literature that help students proactively learn code quality by solving refactoring problems. But they suffer false positive and false negative grading issues because they allow freehand coding. We investigated whether refactoring tutors that do not allow freehand coding could be used to help students learn about non-trivial anti-patterns. We developed and deployed two software tutors for refactoring problems that are based on the principle of “refactoring without rewriting code”, and cover a subset of refactoring problems that can be solved using only deletion, duplication, reordering and token-wise editing of lines of code. We investigated whether students needed to learn the anti-patterns covered by the tutors and whether they benefited from using the tutors. In this experience report, we start by describing the tutors – the list of refactoring concepts covered, the user interface, grading, feedback and usage. We report our experience using the tutors over three semesters, which confirmed that both introductory and advanced students needed and benefited from using the tutors despite the limitations of the tutors’ coverage. We reflect on what worked and what did not. The tutors currently cover C++, Java and C#. They are available for free for educational use on the web at auglets.org. 

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2. Behind the Intent of Extract Method Refactoring: A Systematic Literature Review

   https://doi.org/10.1109/TSE.2023.3345800  

   AlOmar, Eman Abdullah; Mkaouer, Mohamed Wiem; Ouni, Ali (April 2024, IEEE Transactions on Software Engineering)

   Background: Code refactoring is widely recognized as an essential software engineering practice to improve the understandability and maintainability of the source code. The Extract Method refactoring is considered as “Swiss army knife” of refactorings, as developers often apply it to improve their code quality, e.g., decompose long code fragments, reduce code complexity, eliminate duplicated code, etc. In recent years, several studies attempted to recommend Extract Method refactorings allowing the collection, analysis, and revelation of actionable data-driven insights about refactoring practices within software projects. Aim: In this paper, we aim at reviewing the current body of knowledge on existing Extract Method refactoring research and explore their limitations and potential improvement opportunities for future research efforts. That is, Extract Method is considered one of the most widely-used refactorings, but difficult to apply in practice as it involves low-level code changes such as statements, variables, parameters, return types, etc. Hence, researchers and practitioners begin to be aware of the state-of-the-art and identify new research opportunities in this context. Method: We review the body of knowledge related to Extract Method refactoring in the form of a systematic literature review (SLR). After compiling an initial pool of 1,367 papers, we conducted a systematic selection and our final pool included 83 primary studies. We define three sets of research questions and systematically develop and refine a classification schema based on several criteria including their methodology, applicability, and degree of automation. Results: The results construct a catalog of 83 Extract Method approaches indicating that several techniques have been proposed in the literature. Our results show that: (i) 38.6% of Extract Method refactoring studies primarily focus on addressing code clones; (ii) Several of the Extract Method tools incorporate the developer's involvement in the decision-making process when applying the method extraction, and (iii) the existing benchmarks are heterogeneous and do not contain the same type of information, making standardizing them for the purpose of benchmarking difficult. Conclusions: Our study serves as an “index” to the body of knowledge in this area for researchers and practitioners in determining the Extract Method refactoring approach that is most appropriate for their needs. Our findings also empower the community with information to guide the future development of refactoring tools. 

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3. Refactoring a Full Stack Web Application to Remove Barriers for Student Developers and to Add Customization for Instructors

   Cook, Jack; Weiss, Richard; Mache, Jens (January 2020, Journal of computing sciences in colleges)

   null (Ed.)

   This paper describes our experience refactoring EDURange, a fullstack Web application, in order to make it easier for students to do undergraduate research and contribute. As a result, more students were able to contribute to this open source project. In addition, as instructors we wanted to have a simple interface to customize existing exercises and parameterize them so that students could repeat an exercise without it being identical. The main differences were: changing from Ruby on Rails to Python Flask, changing from Virtual Machines to Docker containers, and eliminating dependence on AWS through Terraform. These changes reduced the number of lines of code from 28K to 12K. 1 

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4. On the use of static analysis to engage students with software quality improvement: An experience with PMD

   https://doi.org/10.1109/ICSE-SEET58685.2023.00023  

   AlOmar, Eman Abdullah; AlOmar, Salma Abdullah; Mkaouer, Mohamed Wiem (May 2023, IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET))

   Static analysis tools are frequently used to scan the source code and detect deviations from the project coding guidelines. Given their importance, linters are often introduced to classrooms to educate students on how to detect and potentially avoid these code anti-patterns. However, little is known about their effectiveness in raising students’ awareness, given that these linters tend to generate a large number of false positives. To increase the awareness of potential coding issues that violate coding standards, in this paper, we aim to reflect on our experience with teaching the use of static analysis for the purpose of evaluating its effectiveness in helping students with respect to improving software quality. This paper discusses the results of an experiment in the classroom, over a period of 3 academic semesters, involving 65 submissions that carried out code review activity of 690 rules using PMD. The results of the quantitative and qualitative analysis show that the presence of a set of PMD quality issues influences the acceptance or rejection of the issues, design, and best practices-related categories that take longer time to be resolved, and students acknowledge the potential of using static analysis tools during code review. Through this experiment, code review can turn into a vital part of the educational computing plan. We envision our findings enabling educators to support students with code review strategies in order to raise students’ awareness about static analysis tools and scaffold their coding skills. 

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5. URegM: A Unified Prediction Model of Resource Consumption for Refactoring Software Smells in Open Source Cloud

   Imran, A; Kosar, T (October 2022, Proceedings of ACM European Symposium on Software Engineering (ESSE 2022))

   The low cost and rapid provisioning capabilities have made the cloud a desirable platform to launch complex scientific applications. However, resource utilization optimization is a significant challenge for cloud service providers, since the earlier focus is provided on optimizing resources for the applications that run on the cloud, with a low emphasis being provided on optimizing resource utilization of the cloud computing internal processes. Code refactoring has been associated with improving the maintenance and understanding of software code. However, analyzing the impact of the refactoring source code of the cloud and studying its impact on cloud resource usage require further analysis. In this paper, we propose a framework called Unified Regression Modeling (URegM) which predicts the impact of code smell refactor- ing on cloud resource usage. We test our experiments in a real-life cloud environment using a complex scientific application as a workload. Results show that URegM is capable of accurately predicting resource consumption due to code smell refactoring. This will permit cloud service providers with advanced knowledge about the impact of refactoring code smells on resource consumption, thus allowing them to plan their resource provisioning and code refactoring more effectively. 

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