Software Engineering Using Autonomous Agents: Are We There Yet?

Autonomous agents equipped with Large Language Models (LLMs) are rapidly gaining prominence as a revolutionary technology within the realm of Software Engineering. These intelligent and autonomous systems demonstrate the capacity to perform tasks and make independent decisions, leveraging their intr...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] s. 1855 - 1857
Hlavní autoři: Suri, Samdyuti, Das, Sankar Narayan, Singi, Kapil, Dey, Kuntal, Sharma, Vibhu Saujanya, Kaulgud, Vikrant
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 11.09.2023
Témata:
Autonomous agents
Codes
Complexity theory
Decision making
Large Language Models (LLMs)
SDLC
Software quality
Task analysis
Transforms
ISSN:2643-1572
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Autonomous agents equipped with Large Language Models (LLMs) are rapidly gaining prominence as a revolutionary technology within the realm of Software Engineering. These intelligent and autonomous systems demonstrate the capacity to perform tasks and make independent decisions, leveraging their intrinsic reasoning and decision-making abilities. This paper delves into the current state of autonomous agents, their capabilities, challenges, and opportunities in Software Engineering practices. By employing different prompts (with or without context), we conclude the advantages of contextrich prompts for autonomous agents. Prompts with context enhance user requirement understanding, avoiding irrelevant details that could hinder task comprehension and degrade model performance, particularly when dealing with complex frameworks such as Spring Boot, Django, Flask, etc. This exploration is conducted using Auto-GPT (v0.3.0), an open-source application powered by GPT-3.5 and GPT-4 which intelligently connects the "thoughts" of Large Language Models (LLMs) to independently accomplish the assigned goals or tasks.
ISSN:2643-1572
DOI:10.1109/ASE56229.2023.00174