Summary of Caset: Complexity Analysis Using Simple Execution Traces For Cs* Submissions, by Aaryen Mehta et al.
CASET: Complexity Analysis using Simple Execution Traces for CS* submissions
by Aaryen Mehta, Gagan Aryan
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed CASET tool uses dynamic traces and unsupervised machine learning to analyze the time complexity of algorithms, making it easier for tutors to classify submissions without reading code. This novel approach can improve grading and provide detailed feedback by identifying the algorithms used in student solutions. The tool is particularly useful for CS1 and CS2 courses where students often use different algorithms to solve problems. By analyzing execution traces, CASET can distinguish between submissions that rely on proper algorithmic thinking versus those that simply hard-code results or pattern-match inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The CASET tool helps teachers grade student assignments more efficiently by figuring out which algorithm was used to solve a problem. It does this by looking at how the program runs and using special machine learning techniques. This makes it easier for teachers to give feedback to students, especially in introductory computer science courses where students might not be using the best algorithms. |
Keywords
» Artificial intelligence » Machine learning » Unsupervised
