Abstract
Large language models trained on massive amounts of natural language data and code have shown impressive capabilities in automatic code generation scenarios. Development and evaluation of these models has largely been driven by offline functional correctness metrics, which consider a task to be solved if the generated code passes corresponding unit tests. While functional correctness is clearly an important property of a code generation model, we argue that it may not fully capture what programmers value when collaborating with their AI pair programmers. For example, while a nearly correct suggestion that does not consider edge cases may fail a unit test, it may still provide a substantial starting point or hint to the programmer, thereby reducing total needed effort to complete a coding task. To investigate this, we conduct a user study with (N=49) experienced programmers, and find that while both correctness and effort correlate with value, the association is strongest for effort. We argue that effort should be considered as an important dimension of evaluation in code generation scenarios. We also find that functional correctness remains better at identifying the highest-value generations; but participants still saw considerable value in code that failed unit tests. Conversely, similarity-based metrics are very good at identifying the lowest-value generations among those that fail unit tests. Based on these findings, we propose a simple hybrid metric, which combines functional correctness and similarity-based metrics to capture different dimensions of what programmers might value and show that this hybrid metric more strongly correlates with both value and effort. Our findings emphasize the importance of designing human-centered metrics that capture what programmers need from and value in their AI pair programmers.
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URL
https://arxiv.org/abs/2210.16494
