Reducing the environmental impact of AI-based software systems has become critical. The intensive use of large language models (LLMs) in software engineering poses severe challenges regarding computational resources, data centers, and carbon emissions. In this paper, we investigate how prompt engineering techniques (PETs) can impact the carbon emission of the Llama 3 model for the code generation task. We experimented with the CodeXGLUE benchmark to evaluate both energy consumption and the accuracy of the generated code using an isolated testing environment. Our initial results show that the energy consumption of LLMs can be reduced by using specific tags that distinguish different prompt parts. Even though a more in-depth evaluation is needed to confirm our findings, this work suggests that prompt engineering can reduce LLMs' energy consumption during the inference phase without compromising performance, paving the way for further investigations.
From Prompting to Engineering: A Research Agenda for Prompt Engineering in Software Engineering
Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；CNR（意大利国家研究委员会）；Pegaso University（佩加索大学）；University of Gothenburg and Chalmers University of Technology（哥德堡大学与查尔姆斯理工大学）；Politecnico di Torino（都灵理工大学）；University of Glasgow（格拉斯哥大学）；Tampere University（坦佩雷大学）
TraceLLM: Leveraging Large Language Models with Prompt Engineering for Enhanced Requirements Traceability
KFUPM（国王法赫德石油矿物大学）；Imam Abdulrahman Bin Faisal University（伊玛目阿卜杜勒拉赫曼·本·费萨尔大学）；SDAIA–KFUPM Joint Research Center of Artificial Intelligence, KFUPM（沙特数据与人工智能中心-国王法赫德石油矿物大学人工智能联合研究中心）；University of Quebec in Montreal（蒙特利尔魁北克大学）
Predicting LLM Performance from Prompt Linguistic Features: An Empirical Study in Requirements Engineering
Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Istituto di Linguistica Computazionale “A. Zampolli” (CNR-ILC)（“A.赞波利”计算语言学研究所（意大利国家研究委员会））；University of Padua（帕多瓦大学）；University College Dublin（都柏林大学学院）
BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification
University of Padova（帕多瓦大学）；Imam Mohammad Ibn Saud Islamic University (IMSIU)（伊玛目穆罕默德·本·沙特伊斯兰大学）；Lero, the Research Ireland Centre for Software, University of Limerick（利默里克大学软件研究爱尔兰中心 Lero）；University of Manchester（曼彻斯特大学）；University College Dublin (UCD)（都柏林大学学院）；Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” (ISTI), Consiglio Nazionale delle Ricerche (CNR)（意大利国家研究委员会信息科学与技术研究所 ISTI）
When Prompt Engineering Meets Software Engineering: CNL-P as Natural and Robust "APIs'' for Human-AI Interaction
CSIRO’s Data61（CSIRO数据61实验室）；Jiangxi Provincial Key Laboratory for High Performance Computing（江西省高性能计算重点实验室）；State International Science & Technology Cooperation Base of Networked Supporting Software（网络化支持软件国际科技合作基地）；Language Intelligence Research Center（语言智能研究中心）；Jiangxi Normal University（江西师范大学）；Institute of AI, Newcastle University, UK（人工智能研究院，新castle大学，英国）；UGAiForge
Prompt Engineering Strategies for LLM-based Qualitative Coding of Psychological Safety in Software Engineering Communities: A Controlled Empirical Study
Federal University of Bahia（巴伊亚联邦大学）；State University of Feira de Santana（费拉德桑塔纳州立大学）；Federal University of Sergipe（塞格皮联邦大学）；Federal Institute of Ceara（塞阿拉联邦理工学院）
Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study
Nanyang Technological University（南洋理工大学）；University of New South Wales（新南威尔士大学）；Virginia Tech（弗吉尼亚理工大学）；Huazhong University of Science and Technology（华中科技大学）
