Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products
- URL: http://arxiv.org/abs/2410.12071v2
- Date: Wed, 04 Dec 2024 16:20:40 GMT
- Title: Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products
- Authors: Nadia Nahar, Christian Kästner, Jenna Butler, Chris Parnin, Thomas Zimmermann, Christian Bird,
- Abstract summary: Large Language Models (LLMs) are increasingly embedded into software products across diverse industries.
This study explores the emerging solutions that software developers are adopting to navigate the encountered challenges.
- Score: 21.486150701178154
- License:
- Abstract: Large Language Models (LLMs) are increasingly embedded into software products across diverse industries, enhancing user experiences, but at the same time introducing numerous challenges for developers. Unique characteristics of LLMs force developers, who are accustomed to traditional software development and evaluation, out of their comfort zones as the LLM components shatter standard assumptions about software systems. This study explores the emerging solutions that software developers are adopting to navigate the encountered challenges. Leveraging a mixed-method research, including 26 interviews and a survey with 332 responses, the study identifies 19 emerging solutions regarding quality assurance that practitioners across several product teams at Microsoft are exploring. The findings provide valuable insights that can guide the development and evaluation of LLM-based products more broadly in the face of these challenges.
Related papers
- Seeker: Enhancing Exception Handling in Code with LLM-based Multi-Agent Approach [54.03528377384397]
In real world software development, improper or missing exception handling can severely impact the robustness and reliability of code.
We explore the use of large language models (LLMs) to improve exception handling in code.
We propose Seeker, a multi agent framework inspired by expert developer strategies for exception handling.
arXiv Detail & Related papers (2024-10-09T14:45:45Z) - An Overview and Catalogue of Dependency Challenges in Open Source Software Package Registries [52.23798016734889]
This article provides a catalogue of dependency-related challenges that come with relying on OSS packages or libraries.
The catalogue is based on the scientific literature on empirical research that has been conducted to understand, quantify and overcome these challenges.
arXiv Detail & Related papers (2024-09-27T16:20:20Z) - An Empirical Study on Challenges for LLM Application Developers [28.69628251749012]
We crawl and analyze 29,057 relevant questions from a popular OpenAI developer forum.
After manually analyzing 2,364 sampled questions, we construct a taxonomy of challenges faced by LLM developers.
arXiv Detail & Related papers (2024-08-06T05:46:28Z) - What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions.
We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types.
We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
arXiv Detail & Related papers (2024-07-08T17:27:17Z) - Are LLMs Correctly Integrated into Software Systems? [6.588605888228515]
Large language models (LLMs) provide effective solutions in various application scenarios, with the support of retrieval-augmented generation (RAG)
We have conducted a comprehensive study of 100 open-source applications that incorporate LLMs with RAG support, and identified 18 defect patterns.
Our study reveals that 77% of these applications contain more than three types of integration defects that degrade software functionality, efficiency, and security.
arXiv Detail & Related papers (2024-07-06T17:25:11Z) - Agent-Driven Automatic Software Improvement [55.2480439325792]
This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs)
The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation.
We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
arXiv Detail & Related papers (2024-06-24T15:45:22Z) - A State-of-the-practice Release-readiness Checklist for Generative AI-based Software Products [8.986278918477595]
This paper investigates the complexities of integrating Large Language Models into software products, with a focus on the challenges encountered for determining their readiness for release.
Our systematic review of grey literature identifies common challenges in deploying LLMs, ranging from pre-training and fine-tuning to user experience considerations.
The study introduces a comprehensive checklist designed to guide practitioners in evaluating key release readiness aspects such as performance, monitoring, and deployment strategies.
arXiv Detail & Related papers (2024-03-27T19:02:56Z) - Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study [72.24266814625685]
We explore the performance of large language models (LLMs) across the entire software development lifecycle with DevEval.
DevEval features four programming languages, multiple domains, high-quality data collection, and carefully designed and verified metrics for each task.
Empirical studies show that current LLMs, including GPT-4, fail to solve the challenges presented within DevEval.
arXiv Detail & Related papers (2024-03-13T15:13:44Z) - Competition-Level Problems are Effective LLM Evaluators [121.15880285283116]
This paper aims to evaluate the reasoning capacities of large language models (LLMs) in solving recent programming problems in Codeforces.
We first provide a comprehensive evaluation of GPT-4's peiceived zero-shot performance on this task, considering various aspects such as problems' release time, difficulties, and types of errors encountered.
Surprisingly, theThoughtived performance of GPT-4 has experienced a cliff like decline in problems after September 2021 consistently across all the difficulties and types of problems.
arXiv Detail & Related papers (2023-12-04T18:58:57Z) - Large Language Models for Software Engineering: Survey and Open Problems [35.29302720251483]
This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE)
Our survey reveals the pivotal role that hybrid techniques (traditional SE plus LLMs) have to play in the development and deployment of reliable, efficient and effective LLM-based SE.
arXiv Detail & Related papers (2023-10-05T13:33:26Z) - Software Testing with Large Language Models: Survey, Landscape, and
Vision [32.34617250991638]
Pre-trained large language models (LLMs) have emerged as a breakthrough technology in natural language processing and artificial intelligence.
This paper provides a comprehensive review of the utilization of LLMs in software testing.
arXiv Detail & Related papers (2023-07-14T08:26:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.