Kim Mens
Siegfried Nijssen
ABSTRACT
Research on source code mining has been explored to discover interesting structural regularities, API usage patterns, refactoring opportunities, bugs, crosscutting concerns, code clones and systematic changes. In this paper we present a pattern mining algorithm that uses frequent tree mining to mine for interesting good, bad or ugly coding idioms made by undergraduate students taking an introductory programming course. We do so by looking for patterns that distinguish positive examples, corresponding to the more correct answers to a question, from negative examples, corresponding to solutions that failed the question. We report promising initial results of this algorithm applied to the source code of over 500 students. Even though more work is needed to fine-tune and validate the algorithm further, we hope that it can lead to interesting insights that can eventually be integrated into an intelligent recommendation system to help students learn from their errors.
- Tatsuya Asai, Kenji Abe, Shinji Kawasoe, Hiroshi Sakamoto, Hiroki Arimura, and Setsuo Arikawa. 2004. Efficient substructure discovery from large semi-structured data. IEICE TRANSACTIONS on Information and Systems, 87, 12 (2004), 2754–2763.Google Scholar
- Céline Deknop, Simon Baars, Kim Mens, Ana Oprescu, and Johan Fabry. 2019. Clone Detection vs. Pattern Mining: The Battle. In 18th Belgium-Netherlands Software Evolution Workshop (BENEVOL2019). CEUR Workshop Proc. 2605.Google Scholar
- Guillaume Derval, Anthony Gego, Pierre Reinbold, Benjamin Frantzen, and Peter Van Roy. 2015. Automatic grading of programming exercises in a MOOC using the INGInious platform. European Stakeholder Summit on experiences and best practices in and around MOOCs (EMOOCS’15), 86–91.Google Scholar
- Neil Fraser. 2015. Ten things we’ve learned from Blockly. In 2015 IEEE Blocks and Beyond Workshop (Blocks and Beyond). 49–50. https://doi.org/10.1109/BLOCKS.2015.7369000 Google ScholarDigital Library
- Philip J. Guo. 2013. Online Python Tutor: Embeddable Web-Based Program Visualization for Cs Education. In 44th ACM Technical Symposium on Computer Science Education (SIGCSE ’13). ACM, 579–584. isbn:9781450318686 https://doi.org/10.1145/2445196.2445368 Google ScholarDigital Library
- Aída Jiménez, Fernando Berzal, and Juan Carlos Cubero Talavera. 2010. Frequent tree pattern mining: A survey. Intell. Data Anal., 14, 6 (2010), 603–622. https://doi.org/DOI: 10.1007/978-3-319-07821-2_2Google ScholarCross Ref
- Michael Kölling, Bruce Quig, Andrew Patterson, and John Rosenberg. 2003. The BlueJ System and its Pedagogy. Computer Science Education, 13, 4 (2003), 249–268. https://doi.org/10.1076/csed.13.4.249.17496 Google ScholarCross Ref
- Thomas Lancaster and Fintan Culwin. 2004. A Comparison of Source Code Plagiarism Detection Engines. Computer Science Education, 14, 2 (2004), 101–112. https://doi.org/10.1080/08993400412331363843 Google ScholarCross Ref
- Kim Mens and Angela Lozano. 2014. Source Code-Based Recommendation Systems. In Recommendation Systems in Software Engineering, Martin P. Robillard, Walid Maalej, Robert J. Walker, and Thomas Zimmermann (Eds.). Springer Berlin Heidelberg, 93–130. isbn:978-3-642-45135-5 https://doi.org/10.1007/978-3-642-45135-5_5 Google ScholarCross Ref
- Siegfried Nijssen and Joost N. Kok. 2005. Multi-class Correlated Pattern Mining. In Knowledge Discovery in Inductive Databases, 4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers, Francesco Bonchi and Jean-François Boulicaut (Eds.) (Lecture Notes in Computer Science, Vol. 3933). Springer, 165–187. https://doi.org/10.1007/11733492_10 Google ScholarDigital Library
- Hoang Son Pham, Siegfried Nijssen, Kim Mens, Dario Di Nucci, Tim Molderez, Coen De Roover, Johan Fabry, and Vadim Zaytsev. 2019. Mining Patterns in Source Code Using Tree Mining Algorithms. In Discovery Science. Springer. isbn:978-3-030-33778-0 https://doi.org/10.1007/978-3-030-33778-0_35 Google ScholarDigital Library
- Lutz Prechelt, Guido Malpohl, and M. Philippsen. 2002. Finding Plagiarisms among a Set of Programs with JPlag. Journal of Universal Computer Science, 8 (2002), 1016–1038. https://doi.org/10.3217/jucs-008-11-1016 Google ScholarCross Ref
- Ricardo Alexandre Peixoto Queirós and José Paulo Leal. 2012. PETCHA: A Programming Exercises Teaching Assistant. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE’12). ACM, 192–197. isbn:9781450312462 https://doi.org/10.1145/2325296.2325344 Google ScholarDigital Library
Index Terms
- The good, the bad, and the ugly: mining for patterns in student source code
Recommendations
Mining Patterns in Source Code Using Tree Mining Algorithms
Discovery ScienceAbstractDiscovering regularities in source code is of great interest to software engineers, both in academia and in industry, as regularities can provide useful information to help in a variety of tasks such as code comprehension, code refactoring, and ...
An efficient and effective algorithm for mining top-rank-k frequent patterns
Using N-list structure for mining top-rank-k frequent patterns effectively.Subsume concept was also used to speed up the runtime of the mining process.The experiment was conducted to show the effectiveness of the proposed algorithm. Frequent pattern ...
Comparison and evaluation of source code mining tools and techniques: A qualitative approach
Program source code substantially is structured and contains semantically rich programming constructs such as variables, functions, data structures, and program structures which indicate patterns. Mining source code by using different data mining ...
Comments