Leveraging Large Language Models for Software Verification and Falsification: A Taxonomy of Downstream Tasks

Core Concepts

This work proposes a taxonomy of downstream tasks that capture how researchers and practitioners have been leveraging prompts to utilize the emergent capabilities of Large Language Models (LLMs) for software testing, verification, and related problems.

Abstract

The paper investigates how software testing and verification research communities have been using prompts to leverage the capabilities of Large Language Models (LLMs). The authors first validate whether the concept of "downstream tasks" is adequate to convey the blueprint of prompt-based solutions. They then develop a novel taxonomy of downstream tasks to identify patterns and commonalities in a varied spectrum of Software Engineering problems, including testing, fuzzing, debugging, vulnerability detection, static analysis, and program verification.
The taxonomy is organized hierarchically, with top-level categories capturing different high-level conceptual operations, such as Generative, Evaluative, Extractive, Abstractive, Executive, and Consultative tasks. These categories are further divided into more specific task families based on the type of software artifacts being processed and the nature of the operations performed.
The authors provide detailed tables that summarize the downstream tasks elicited by prompts in various LLM-enabled approaches for software testing, fuzzing, debugging, vulnerability detection, static analysis, and program verification. These tables describe the input-output relationships of the tasks, as well as the integration and orchestration of the LLM components within the overall approach.
The proposed taxonomy helps identify patterns, trends, and unexplored areas in the use of LLMs for software engineering problems. It also provides a framework for discussing design patterns of LLM-enabled approaches, characteristics of task families, and opportunities for future research and development.

Stats

"Prompting has become one of the main approaches to leverage emergent capabilities of Large Language Models [Brown et al. NeurIPS 2020, Wei et al. TMLR 2022, Wei et al. NeurIPS 2022]."
"We were able to recover from the 80 reported papers their downstream tasks and present them homogeneously no matter how sophisticated the underlying probabilistic program is."
"Identified downstream tasks end up being rich in terms nature and functional features and, to the best of our knowledge, some of them were not previously identified in existing taxonomies."

Quotes

"Taxonomies may result in rigid concepts that do not favour the use of versatility of concrete concepts and phenomena like, in this case, inference elicited by prompts. However, we believe abstract organization is worth its risks: one could see patterns, trends, unexplored spots, and a way to recognize when one is in front of a 'brand new specimen' or category of things."
"Even in the case were fine-tuned neural paradigms were already in place (e.g., vulnerability detection), there is an apparent gap between expected LLMs proficiency and the nature of problems and 'classical' solutions (and, thus, one would expect ingenious LLM-enabled solutions)."

Key Insights Distilled From

Tasks People Prompt: A Taxonomy of LLM Downstream Tasks in Software Verification and Falsification Approaches

by Víct... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09384.pdf

Deeper Inquiries

How can the proposed taxonomy be extended to capture the versatility and dynamic nature of prompt-based interactions with LLMs, beyond the static view of downstream tasks?

The proposed taxonomy can be extended by incorporating additional dimensions that capture the dynamic nature of prompt-based interactions with LLMs. One way to achieve this is by introducing a temporal aspect to the taxonomy, where tasks are not only categorized based on their nature but also on when they occur in the interaction with the LLM. This temporal dimension can include stages such as initialization, prompt generation, response processing, and feedback incorporation. By including this temporal aspect, the taxonomy can better represent the flow of interactions between the user and the LLM.
Furthermore, the taxonomy can be extended to include meta-tasks that govern the orchestration and coordination of multiple downstream tasks. These meta-tasks can capture higher-level behaviors such as task sequencing, task prioritization, and task refinement based on feedback. By including meta-tasks, the taxonomy can provide a more holistic view of how prompt-based interactions with LLMs are structured and managed.
Additionally, the taxonomy can incorporate a feedback loop mechanism that captures how the outcomes of downstream tasks influence subsequent interactions and prompts with the LLM. This feedback loop dimension can highlight the adaptive nature of prompt-based interactions and how the system learns and improves over time based on past interactions.

What are the potential limitations of the task-oriented documentation approach, and how can it be complemented with other perspectives to provide a more comprehensive understanding of LLM-enabled software engineering solutions?

One potential limitation of the task-oriented documentation approach is that it may oversimplify the complex interactions and decision-making processes involved in prompt-based interactions with LLMs. By focusing solely on downstream tasks, the approach may overlook the nuances of prompt generation, response interpretation, and feedback integration, which are crucial aspects of effective LLM utilization.
To complement the task-oriented documentation approach, other perspectives can be incorporated to provide a more comprehensive understanding of LLM-enabled software engineering solutions. One such perspective is a user-centric view, which focuses on the human-computer interaction aspects of prompt-based interactions. This perspective can shed light on user preferences, cognitive load, and usability challenges associated with interacting with LLMs in a software engineering context.
Another complementary perspective is a system-level view, which considers the integration of LLMs within the broader software development ecosystem. This perspective can explore how LLMs interact with existing tools, processes, and workflows in software engineering practices. By examining the system-level implications, the approach can uncover dependencies, constraints, and opportunities for optimizing LLM-enabled solutions.
Furthermore, a performance evaluation perspective can be valuable in assessing the effectiveness and efficiency of LLM-enabled software engineering solutions. This perspective can involve benchmarking, comparative studies, and metrics-based evaluations to quantify the impact of LLM integration on software development outcomes.

Given the rapid advancements in LLM capabilities, how might the taxonomy and the identified patterns evolve in the future, and what new task categories or families might emerge as LLMs are further integrated into software engineering workflows?

As LLM capabilities continue to advance, the taxonomy and identified patterns are likely to evolve to accommodate new functionalities and use cases. One potential evolution is the emergence of meta-tasks that oversee the orchestration of complex interactions with LLMs, such as task planning, adaptive prompting strategies, and feedback-driven learning mechanisms. These meta-tasks can provide a higher-level abstraction of the interaction process and enable more sophisticated control over LLM behavior.
Additionally, new task categories or families may emerge to address specialized software engineering challenges, such as automated code refactoring, natural language interface generation, or adaptive test case generation. These new categories can reflect the expanding capabilities of LLMs in understanding and generating diverse types of software artifacts.
Furthermore, with the increasing integration of LLMs into software engineering workflows, hybrid approaches that combine LLM capabilities with traditional software development practices may become more prevalent. This integration can lead to the emergence of hybrid task categories that leverage the strengths of both LLMs and conventional tools to enhance software engineering processes.
Overall, the taxonomy and identified patterns are expected to adapt to the evolving landscape of LLM-enabled software engineering solutions, reflecting the growing sophistication and versatility of LLM capabilities in addressing diverse software development challenges.