Affiliation:
1. Korea University, South Korea
Abstract
We present FixML, a system for automatically generating feedback on logical errors in functional programming assignments. As functional languages have been gaining popularity, the number of students enrolling functional programming courses has increased significantly. However, the quality of feedback, in particular for logical errors, is hardly satisfying. To provide personalized feedback on logical errors, we present a new error-correction algorithm for functional languages, which combines statistical error-localization and type-directed program synthesis enhanced with components reduction and search space pruning using symbolic execution. We implemented our algorithm in a tool, called FixML, and evaluated it with 497 students’ submissions from 13 exercises, including not only introductory but also more advanced problems. Our experimental results show that our tool effectively corrects various and complex errors: it fixed 43% of the 497 submissions in 5.4 seconds on average and managed to fix a hard-to-find error in a large submission, consisting of 154 lines. We also performed user study with 18 undergraduate students and confirmed that our system actually helps students to better understand their programming errors.
Publisher
Association for Computing Machinery (ACM)
Subject
Safety, Risk, Reliability and Quality,Software
Reference46 articles.
1. Recursive Program Synthesis
2. From symptom to cause
3. Matej Balog Alexander L. Gaunt Marc Brockschmidt Sebastian Nowozin and Daniel Tarlow. 2017. DeepCoder: Learning to Write Programs. In ICLR. Matej Balog Alexander L. Gaunt Marc Brockschmidt Sebastian Nowozin and Daniel Tarlow. 2017. DeepCoder: Learning to Write Programs. In ICLR.
4. Neuro-symbolic program corrector for introductory programming assignments
5. Counter-factual typing for debugging type errors
Cited by
17 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献