Rodrigo Pato Nogueira

Rodrigo Pato Nogueira, Marco Vieira, João R. Campos

Large Language Models (LLMs) are increasingly being used in everyday software engineering tasks, particularly in automated code generation. Despite their widespread adoption, these models remain far from perfect, making systematic and fair evaluation essential to understand their strengths and limitations. In the context of code generation, existing benchmarks are limited: they often target a single programming language and rely primarily on unit test outcomes, while overlooking other critical dimensions such as the overall quality of the generated code and its closeness to a valid solution. To address these gaps, we introduce PROBE, an extensible benchmark framework that, unlike prior work, establishes a systematic structure built on diverse and well-defined metrics, representative workloads, varied prompt templates, and a robust experimental procedure. In practice, the code generated by the LLMs is evaluated along three complementary dimensions: functional correctness, proximity to valid solutions, and code quality, enabling a comprehensive assessment of performance. We use PROBE to evaluate four open-source and two proprietary models under three prompting strategies across five programming languages. We further complement this analysis with a study of common errors in the code and provide concrete examples, offering clearer insight into where LLMs tend to struggle. Our findings show that, while LLMs achieve promising results, they struggle with harder problems and, in the case of smaller models, with programming languages that have fewer available resources for training, and they often fail due to fundamental and easily avoidable errors that underscore the unreliability of automatically generated code.

Rodrigo Pato Nogueira, Karthik Pattabiraman, Marco Vieira et al.

N-Version Programming (NVP) improves software reliability by executing multiple independent implementations and combining outputs, but its adoption is limited by high cost and the assumption of failure independence, which empirical studies have challenged. Recent advances in Large Language Models (LLMs) reduce the cost of generating multiple implementations, shifting focus to whether their failures are independent. We propose the first systematic methodology to assess failure independence in LLM-generated code and apply it to 224 problems across twelve models, five languages, and three prompting strategies. We analyze both structural and behavioral diversity (i.e., whether implementations fail on the same test cases), complemented by N-version reliability analysis under majority voting and manual inspection of the generated code. Structural diversity analysis shows that implementations from the same model are highly similar, while different models produce more distinct solutions. The same trend appears in behavioral diversity, with implementations from different models showing higher diversity yet still failing on the same tests far more often than expected under independence. N-version reliability analysis reinforces this: three- and five-version ensembles realize only 0.43 and 0.44 of the reliability gain achievable under independence, dropping below 0.3 when ensembles are built from the same model. Manual fault analysis shows that even different failure patterns often share root causes. Overall, these results suggest LLM-generated solutions do not satisfy NVP's failure independence assumption, though heterogeneous models help partially. They also validate our methodology as a tool for systematically evaluating failure independence as models evolve.