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Analysing Student Code Submissions Using Program Analysis Techniques and Large Language Models
Thesis (MSc)--Stellenbosch University, 2026.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2026
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| _version_ | 1867614000098836480 |
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| access_status_str | Open Access |
| author | Grobbelaar, Nelia |
| author2 | Inggs, Cornelia P. |
| author_browse | Grobbelaar, Nelia Inggs, Cornelia P. |
| author_facet | Inggs, Cornelia P. Grobbelaar, Nelia |
| author_sort | Grobbelaar, Nelia |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch University, 2026. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/136044 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:45:04.096Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/136044 Analysing Student Code Submissions Using Program Analysis Techniques and Large Language Models Grobbelaar, Nelia Inggs, Cornelia P. Bester, Willem H. K. Stellenbosch University. Faculty of Science. Dept. of Computer Science. Thesis (MSc)--Stellenbosch University, 2026. Grobbelaar, N. 2026. Analysing Student Code Submissions Using Program Analysis Techniques and Large Language Models. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/ab51fcbd-3fdd-43cb-ab70-d60ea7557cc1 Programming students struggle to interpret technical diagnostics from static analysis tools, while large language models provide accessible explanations but lack formal guarantees. This thesis investigates when static analysis grounding—that is, providing the model with structured tool output (such as compiler warnings or type errors) to anchor its explanations—improves language model feedback across different programming contexts. Two experiments are reported: the first targeted concurrent programming and revealed deployment infrastructure as critical for evaluation; the second compared three feedback strategies for C programming—raw static analysis, analysis with fine-tuned model interpretation, and ungrounded language model—in a six-week classroom deployment across multiple assignments. Grounded feedback produces greater improvement for struggling students than either raw analysis or ungrounded models, with no benefit for competent programmers, suggesting grounding aids interpretation rather than detection: translating diagnostics, highlighting important issues, and connecting errors to concepts. Deployment infrastructure proved more consequential than technical optimisation, with cloud-native architecture enabling successful evaluation while sophisticated local deployment proved unreliable. The work contributes empirical evidence on when grounding benefits students, reusable static analysis infrastructure, and deployment design principles, with approximately 40–50% of infrastructure transferring across contexts and assignment characteristics significantly moderating feedback effectiveness. Masters 2026-04-21T08:45:37Z 2026-04-21T08:45:37Z 2026-03 Thesis https://scholar.sun.ac.za/handle/10019.1/136044 en Stellenbosch University 223 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Grobbelaar, Nelia Analysing Student Code Submissions Using Program Analysis Techniques and Large Language Models |
| title | Analysing Student Code Submissions Using Program Analysis Techniques and Large Language Models |
| title_full | Analysing Student Code Submissions Using Program Analysis Techniques and Large Language Models |
| title_fullStr | Analysing Student Code Submissions Using Program Analysis Techniques and Large Language Models |
| title_full_unstemmed | Analysing Student Code Submissions Using Program Analysis Techniques and Large Language Models |
| title_short | Analysing Student Code Submissions Using Program Analysis Techniques and Large Language Models |
| title_sort | analysing student code submissions using program analysis techniques and large language models |
| url | https://scholar.sun.ac.za/handle/10019.1/136044 |
| work_keys_str_mv | AT grobbelaarnelia analysingstudentcodesubmissionsusingprogramanalysistechniquesandlargelanguagemodels |