Leonardo Mathon
ABSTRACT
No abstract available.
- 2004. SQLator - An online SQL learning workbench. 223–227. https://doi.org/10.1145/1026487.1008055Google Scholar
Digital Library
- Alireza Ahadi, Julia Prior, Vahid Behbood, and Raymond Lister. 2016. Students’ Semantic Mistakes in Writing Seven Different Types of SQL Queries. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (Arequipa, Peru) (ITiCSE ’16). Association for Computing Machinery, New York, NY, USA, 272–277. https://doi.org/10.1145/2899415.2899464Google ScholarDigital Library
- Natalia Arzamasova, Martin Schäler, and Klemens Böhm. 2018. Cleaning Antipatterns in an SQL Query Log. IEEE Transactions on Knowledge and Data Engineering 30, 3(2018), 421–434. https://doi.org/10.1109/TKDE.2017.2772252Google ScholarCross Ref
- Prashanth Dintyala, Arpit Narechania, and Joy Arulraj. 2020. SQLCheck: Automated Detection and Diagnosis of SQL Anti-Patterns. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. ACM. https://doi.org/10.1145/3318464.3389754Google ScholarDigital Library
- Bill Karwin. 2010. SQL Antipatterns: Avoiding the Pitfalls of Database Programming (1st ed.). Pragmatic Bookshelf.Google Scholar
- Andrew Koenig. 1995. Patterns and Antipatterns. J. Object Oriented Program. 8, 1 (1995), 46–48.Google Scholar
- Yingjun Lyu, Ali Alotaibi, and William G. J. Halfond. 2019. Quantifying the Performance Impact of SQL Antipatterns on Mobile Applications. In 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). 53–64. https://doi.org/10.1109/ICSME.2019.00015Google Scholar
- Yingjun Lyu, Sasha Volokh, William G. J. Halfond, and Omer Tripp. 2021. SAND: A Static Analysis Approach for Detecting SQL Antipatterns. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (Virtual, Denmark) (ISSTA 2021). Association for Computing Machinery, New York, NY, USA, 270–282. https://doi.org/10.1145/3460319.3464818Google ScholarDigital Library
- Daphne Miedema, Efthimia Aivaloglou, and George Fletcher. 2021. Identifying SQL Misconceptions of Novices: Findings from a Think-Aloud Study. Vol. 1. Association for Computing Machinery. 355–367 pages. https://doi.org/10.1145/3446871.3469759Google ScholarDigital Library
- Daphne Miedema, George Fletcher, and Efthimia Aivaloglou. 2022. So many brackets! An analysis of how SQL learners (mis)manage complexity during query formulation. In 2021 IEEE/ACM 29th International Conference on Program Comprehension.Google ScholarDigital Library
- Antonija Mitrovic. 1998. Learning SQL with a computerized tutor. In ACM SIGCSE Bulletin. 307–311.Google Scholar
- Biruk Asmare Muse, Mohammad Masudur Rahman, Csaba Nagy, Anthony Cleve, Foutse Khomh, and Giuliano Antoniol. 2020. On the Prevalence, Impact, and Evolution of SQL Code Smells in Data-Intensive Systems. In Proceedings of the 17th International Conference on Mining Software Repositories (Seoul, Republic of Korea) (MSR ’20). Association for Computing Machinery, New York, NY, USA, 327–338. https://doi.org/10.1145/3379597.3387467Google ScholarDigital Library
- Csaba Nagy and Anthony Cleve. 2017. A Static Code Smell Detector for SQL Queries Embedded in Java Code. In 2017 IEEE 17th International Working Conference on Source Code Analysis and Manipulation (SCAM). 147–152. https://doi.org/10.1109/SCAM.2017.19Google ScholarCross Ref
- William C. Ogden and Susan R. Brooks. 1983. Query languages for the casual user. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems - CHI ’83. ACM Press, New York, New York, USA, 161–165. https://doi.org/10.1145/800045.801602Google ScholarDigital Library
- Abdou Ousmane and Hongwei Xie. 2019. Detecting anti-patterns in SQL Queries using Text Classification Techniques. International Journal of Advanced Engineering Research and Science 6 (01 2019), 305–309. https://doi.org/10.22161/ijaers.6.4.35Google ScholarCross Ref
- Kai Presler-Marshall, Sarah Heckman, and Kathryn Stolee. 2021. SQLRepair: Identifying and Repairing Mistakes in Student-Authored SQL Queries. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET). 199–210. https://doi.org/10.1109/ICSE-SEET52601.2021.00030Google ScholarDigital Library
- Phyllis Reisner. 1981. Human Factors Studies of Database Query Languages: A Survey and Assessment. Comput. Surveys 13, 1 (jan 1981), 13–31. https://doi.org/10.1145/356835.356837Google ScholarDigital Library
- Toni Taipalus. 2020. Explaining Causes Behind SQL Query Formulation Errors. In 2020 IEEE Frontiers in Education Conference (FIE). 1–9. https://doi.org/10.1109/FIE44824.2020.9274114Google ScholarDigital Library
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