The article investigates human-AI pair programming (pAIr programming) and discusses its benefits and challenges similar to traditional human-human pair programming. Although both modalities show potential, the research currently lacks clarity about the effectiveness of human-AI interactions. The paper emphasizes the necessity for more realistic study designs and better measurement of quality, productivity, and costs. Factors such as task complexity and communication styles are examined, suggesting that these moderators can significantly affect the outcomes of pairing strategies. Future research should build on these findings to enhance pAIr programming designs based on existing human-human pair programming literature.
The paper explores human-AI pair programming (pAIr), revealing mixed outcomes regarding quality, productivity, and learning, needing more valid measures for effective evaluation.
Insights from human-human pair programming should guide the design of human-AI pair programming efforts, particularly regarding realistic study designs and the evaluation of outcomes and measures.
The current research lacks definitive conclusions on the efficacy of human-AI pair programming, highlighting the need for comprehensive measurement approaches to assess its effectiveness.
A deeper investigation into task types, compatibility, and communication is essential to understand how they influence the pAIr programming process and its outcomes.
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