activity
20222026
most citedDo Generative AI Tools Ensure Green Code? An Investigative Study

11 citations · 27 across the 10 of their papers we have counts for

collaborators

10 papers

cs.SE2026

Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development

Rohit Mehra, Kapil Singi, Vikrant Kaulgud +5

Prompt engineering has emerged as a critical yet undertaught skill for software developers, one that traditional learning approaches are ill-equipped to support given its evolving,…

cs.SE2026★ 1 cited

SEALing the Gap: A Reference Framework for LLM Inference Carbon Estimation via Multi-Benchmark Driven Embodiment

Priyavanshi Pathania, Rohit Mehra, Vibhu Saujanya Sharma +4

Large Language Models are rapidly gaining traction in software engineering, yet their growing carbon footprint raises pressing sustainability concerns. While training emissions are…

cs.SE2025

Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms

Rajrupa Chattaraj, Sridhar Chimalakonda, Vibhu Saujanya Sharma +1

The utilization of Machine Learning (ML) in contemporary software systems is extensive and continually expanding. However, its usage is energy-intensive, contributing to increased…

cs.SE2025★ 2 cited

Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead

Priyavanshi Pathania, Nikhil Bamby, Rohit Mehra +5

The proliferation of software and AI comes with a hidden risk: its growing energy and carbon footprint. As concerns regarding environmental sustainability come to the forefront, un…

cs.SE2025★ 3 cited

Assessing the Impact of Refactoring Energy-Inefficient Code Patterns on Software Sustainability: An Industry Case Study

Rohit Mehra, Priyavanshi Pathania, Vibhu Saujanya Sharma +3

Advances in technologies like artificial intelligence and metaverse have led to a proliferation of software systems in business and everyday life. With this widespread penetration,…

cs.SE2025★ 2 cited

Towards a Knowledge Base of Common Sustainability Weaknesses in Green Software Development

Priyavanshi Pathania, Rohit Mehra, Vibhu Saujanya Sharma +3

With the climate crisis looming, engineering sustainable software systems become crucial to optimize resource utilization, minimize environmental impact, and foster a greener, more…

cs.SE2025★ 11 cited

Do Generative AI Tools Ensure Green Code? An Investigative Study

Samarth Sikand, Rohit Mehra, Vibhu Saujanya Sharma +3

Software sustainability is emerging as a primary concern, aiming to optimize resource utilization, minimize environmental impact, and promote a greener, more resilient digital ecos…

cs.LG2025

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs

Samarth Sikand, Rohit Mehra, Priyavanshi Pathania +5

While Generative AI stands to be one of the fastest adopted technologies ever, studies have made evident that the usage of Large Language Models (LLMs) puts significant burden on e…

cs.SE2022★ 4 cited

ESAVE: Estimating Server and Virtual Machine Energy

Priyavanshi Pathania, Rohit Mehra, Vibhu Saujanya Sharma +3

Sustainable software engineering has received a lot of attention in recent times, as we witness an ever-growing slice of energy use, for example, at data centers, as software syste…

cs.SE2022★ 4 cited

Towards a Green Quotient for Software Projects

Rohit Mehra, Vibhu Saujanya Sharma, Vikrant Kaulgud +2

As sustainability takes center stage across businesses, green and energy-efficient choices are more crucial than ever. While it is becoming increasingly evident that software and t…