Prompting Matters: Assessing the Effect of Prompting Techniques on LLM-Generated Class Code
Bibliographic record
Abstract
The field of software engineering and coding has undergone a significant transformation. The integration of large language models (LLMs), such as ChatGPT, into software development workflows is changing how developers at all skill levels approach coding tasks. Leveraging the capabilities of LLMs, developers can now implement functionalities, fix bugs, and address reviewers' comments more efficiently. However, prior research shows that the effectiveness of LLM-generated code is heavily influenced by the prompting strategy used. Furthermore, generating code at the class level is significantly more complex than at the method level, as it requires maintaining consistency across multiple methods and managing class state. Therefore, this study evaluates the impact of four prompting strategies (i.e., Zero-Shot, Few-Shot, Chain-of-Thought, and Chain-of-Thought-Few-Shot) on GPT and Llama3 in generating class-level code. It assesses the functional correctness and the quality characteristics of the generated code. To better understand how errors differ by prompting strategy, a qualitative analysis of the generated code is conducted for test cases that fail. The findings show that strategies incorporating more contextual guidance (Few-Shot, Chain-of-Thought, and Chain-of-Thought Few-Shot) outperform Zero-Shot prompting by up to 25% in functional correctness, 31% in BLEU-3 score, and 50% in ROUGE-L, while also producing code that is more readable and maintainable. The results also indicate that procedural logic and control flow errors are the most prominent, accounting for 31% of all errors. This study provides valuable insights to guide future research in developing techniques and tools that enhance the quality and reliability of LLM-generated code for complex software development tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".