Autonomous agents equipped with Large Language Models (LLMs) are rapidly gaining prominence as a revolutionary technology within the realm of Software Engineering. These intelligent systems demonstrate the capacity to autonomously perform tasks and make independent decisions, leveraging their intrinsic reasoning and decision-making abilities. This paper addresses an examination that involves delving into the current state of autonomous agents including their capabilities, challenges, and opportunities in Software Engineering practices, with special emphasis on Auto-GPT.

Samdyuti Suri

Accenture Tech Labs

Sankar Narayan Das

Accenture Tech Labs

Kapil Singi

Accenture

Kuntal Dey

Accenture Labs, India

Vibhu Saujanya Sharma

Accenture Labs

Vikrant Kaulgud

Accenture Labs, India

India

Recent breakthroughs in Large Language Models (LLM), comprised of billions of parameters, have achieved the ability to unveil exceptional insight into a wide range of Natural Language Processing (NLP) tasks. The onus of the performance of these models lies in the sophistication and completeness of the input prompt. As such, minimizing the series of prompt enhancements with improvised keywords becomes critically important as it directly affects the time to market and cost of the developing solution. However, this process inevitably has a trade-off between the learning curve/proficiency of the user and completeness of the prompt, as generating absolute solutions is an incremental process. In this paper, we have designed a novel solution and implemented it in the form of a plugin for Visual Studio Code IDE, which can optimize this trade-off, by learning the underline prompt intent to enhance with keywords. This will tend to align with developers’ collection of semantics while developing a secure code, ensuring parameter and local variable names, return expressions, simple pre and post-conditions, and basic control and data flow are met.

Kanchanjot Kaur Phokela

Accenture

India

Samarth Sikand

Accenture Labs

Kapil Singi

Accenture

Kuntal Dey

Accenture Labs, India

Vibhu Saujanya Sharma

Accenture Labs

Vikrant Kaulgud

Accenture Labs, India

India

no description available

Priyavanshi Pathania

Accenture Labs

Rohit Mehra

Accenture Labs

India

Vibhu Saujanya Sharma

Accenture Labs

Vikrant Kaulgud

Accenture Labs, India

India

Sanjay Podder

Accenture

India

Adam P. Burden

Accenture

As the world takes cognizance of AI’s growing role in greenhouse gas(GHG) and carbon emissions, the focus of AI research & development is shifting towards inclusion of energy efficiency as another core metric. Sustainability, a core agenda for most organizations, is also being viewed as a core nonfunctional requirement in software engineering. A similar effort is being undertaken to extend sustainability principles to AI-based systems with focus on energy efficient training and inference techniques. But an important question arises, does there even exist any metrics or methods which can quantify adoption of “green” practices in the life cycle of AI-based systems? There is a huge gap which exists between the growing research corpus related to sustainable practices in AI research and its adoption at an industry scale. The goal of this work is to introduce a methodology and novel metric for assessing ”greenness” of any AI-based system and its development process, based on energy efficient AI research and practices. The novel metric, termed as Green AI Quotient, would be a key step towards AI practitioner’s Green AI journey. Empirical validation of our approach suggest that Green AI Quotient is able to encourage adoption and raise awareness regarding sustainable practices in AI lifecycle.

Samarth Sikand

Accenture Labs

Vibhu Saujanya Sharma

Accenture Labs

Vikrant Kaulgud

Accenture Labs, India

India

Sanjay Podder

Accenture

India

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, the carbon emissions of software are rapidly growing as well, thereby negatively impacting the long-term sustainability of our environment. Hence, optimizing software from a sustainability standpoint becomes more crucial than ever. We believe that the adoption of automated tools that can identify energy-inefficient patterns in the code and guide appropriate refactoring can significantly assist in this optimization. In this extended abstract, we present an industry case study that evaluates the sustainability impact of refactoring energy-inefficient code patterns identified by automated software sustainability assessment tools for a large application. Preliminary results highlight a positive impact on the application’s sustainability post-refactoring, leading to a 29% decrease in energy consumption.

Rohit Mehra

Accenture Labs

India

Priyavanshi Pathania

Accenture Labs

Vibhu Saujanya Sharma

Accenture Labs

Vikrant Kaulgud

Accenture Labs, India

India

Sanjay Podder

Accenture

India

Adam P. Burden

Accenture