More Like this

1. Long Term Retention of Programming Concepts Learned Using Tracing Versus Debugging Tutors

   https://doi.org/10.1007/978-3-030-78270-2_39  

   Kumar, Amruth N. (June 2021, Artificial Intelligence in Education)

   null (Ed.)

   We studied long-term retention of the concepts that introductory programming students learned using two software tutors on tracing the behavior of functions and debugging functions. Whereas the concepts covered by the tutor on the behavior of functions were interdependent, the concepts covered by debugging tutor were independent. We analyzed the data of the students who had used the tutors more than once, hours to weeks apart. Our objective was to find whether students retained what they had learned during the first session till the second session. We found that the more the problems students solved during the first session, the greater the retention. Knowledge and retention varied between debugging and behavior tutors, even though they both dealt with functions, possibly because de-bugging tutor covered independent concepts whereas behavior tutor covered interdependent concepts. 

   more » « less
2. 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. 

   more » « less
3. Automating Source Code Refactoring in the Classroom

   https://doi.org/10.1145/3626252.3630787  

   AlOmar, Eman Abdullah; Mkaouer, Mohamed Wiem; Ouni, Ali (March 2024, ACM The Technical Symposium on Computer Science Education (SIGCSE TS))

   Refactoring is the practice of improving software quality without altering its external behavior. Developers intuitively refactor their code for multiple purposes, such as improving program comprehension, reducing code complexity, dealing with technical debt, and removing code smells. However, no prior studies have exposed the students to an experience of the process of antipatterns detection and refactoring correction, and provided students with toolset to practice it. To understand and increase the awareness of refactoring concepts, in this paper, we aim to reflect on our experience with teaching refactoring and how it helps students become more aware of bad programming practices and the importance of correcting them via refactoring. This paper discusses the results of an experiment in the classroom that involved carrying out various refactoring activities for the purpose of removing antipatterns using JDeodorant, an IDE plugin that supports antipatterns detection and refactoring. The results of the quantitative and qualitative analysis with 171 students show that students tend to appreciate the idea of learning refactoring and are satisfied with various aspects of the JDeodorant plugin's operation. Through this experiment, refactoring can turn into a vital part of the computing educational plan. We envision our findings enabling educators to support students with refactoring tools tuned towards safer and trustworthy refactoring. 

   more » « less
4. The Effectiveness of Visualization for Learning Expression Evaluation: A Reproducibility Study

   https://doi.org/10.1145/2899415.2899427  

   Kumar, Amruth N. (July 2016, Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education)

   A study was conducted to reproduce the results of an earlier study on the effectiveness of visualization for learning expression evaluation in a problem-solving software tutor on arithmetic expressions. In the current reproducibility study, data was collected from a software tutor on assignment expressions over six semesters. ANOVA analysis of the amount and speed of learning was conducted with treatment, sex and racial groups as fixed factors. Results include that visualization helped the students learn significantly more concepts, whether the students needed to use the tutor or benefited from using the tutor. However, it only benefited the less-prepared students. It did not help the students learn faster. It benefited both the sexes and traditionally represented as well as underrepresented groups. The current study confirmed almost all the results from the previous study, albeit for a harder topic. One reason why visualization was found to be effective in both these studies may be that the same visualization scheme was used by the students to both view feedback and construct their answers. 

   more » « less
5. Code Quality Improvement for All: Automated Refactoring for Scratch

   https://doi.org/10.1109/VLHCC.2019.8818950  

   Techapalokul, Peeratham; Tilevich, Eli (October 2019, 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC))

   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. 

   more » « less