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Neural Fault Injection: Generating Software Faults from Natural Language

Domenico Cotroneo, Pietro Liguori

TL;DR

This work tackles the limited realism and customization of traditional software fault injection by introducing an end-to-end methodology that uses Large Language Models (LLMs) augmented with Reinforcement Learning from Human Feedback (RLHF) to generate executable fault scenarios from natural language. The approach comprises an NLP preprocessing stage, an LLM-based fault-code generator, an RLHF-driven refinement loop, and an automated integration/testing tool, enabling rapid, customizable fault synthesis and seamless inclusion in testing pipelines. By building a fault-generation dataset with a Python-based fault-injection tool and illustrating with running examples, the paper demonstrates potential gains in efficiency and fault coverage. The proposed framework aims to make fault injection more representative of real-world risks, reduce manual effort, and better integrate into standard software-testing workflows, thereby improving dependability in complex software systems.

Abstract

Traditional software fault injection methods, while foundational, face limitations in adequately representing real-world faults, offering customization, and requiring significant manual effort and expertise. This paper introduces a novel methodology that harnesses the capabilities of Large Language Models (LLMs) augmented with Reinforcement Learning from Human Feedback (RLHF) to overcome these challenges. The usage of RLHF emphasizes an iterative refinement process, allowing testers to provide feedback on generated faults, which is then used to enhance the LLM's fault generation capabilities, ensuring the generation of fault scenarios that closely mirror actual operational risks. This innovative methodology aims to significantly reduce the manual effort involved in crafting fault scenarios as it allows testers to focus on higher-level testing strategies, hence paving the way to new possibilities for enhancing the dependability of software systems.

Neural Fault Injection: Generating Software Faults from Natural Language

TL;DR

This work tackles the limited realism and customization of traditional software fault injection by introducing an end-to-end methodology that uses Large Language Models (LLMs) augmented with Reinforcement Learning from Human Feedback (RLHF) to generate executable fault scenarios from natural language. The approach comprises an NLP preprocessing stage, an LLM-based fault-code generator, an RLHF-driven refinement loop, and an automated integration/testing tool, enabling rapid, customizable fault synthesis and seamless inclusion in testing pipelines. By building a fault-generation dataset with a Python-based fault-injection tool and illustrating with running examples, the paper demonstrates potential gains in efficiency and fault coverage. The proposed framework aims to make fault injection more representative of real-world risks, reduce manual effort, and better integrate into standard software-testing workflows, thereby improving dependability in complex software systems.

Abstract

Traditional software fault injection methods, while foundational, face limitations in adequately representing real-world faults, offering customization, and requiring significant manual effort and expertise. This paper introduces a novel methodology that harnesses the capabilities of Large Language Models (LLMs) augmented with Reinforcement Learning from Human Feedback (RLHF) to overcome these challenges. The usage of RLHF emphasizes an iterative refinement process, allowing testers to provide feedback on generated faults, which is then used to enhance the LLM's fault generation capabilities, ensuring the generation of fault scenarios that closely mirror actual operational risks. This innovative methodology aims to significantly reduce the manual effort involved in crafting fault scenarios as it allows testers to focus on higher-level testing strategies, hence paving the way to new possibilities for enhancing the dependability of software systems.
Paper Structure (17 sections, 1 figure)

This paper contains 17 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Inadequate Representation of Real-World Faults
  4. Lack of Customization and Flexibility
  5. Manual Effort and Expertise Requirements
  6. Proposed Methodology
  7. Running Example
  8. Implementation
  9. The Natural Language Processing (NLP) Engine
  10. The Large Language Model
  11. RLHF Mechanism
  12. Automated Integration and Testing Tool
  13. Threats to Validity
  14. Data and Training Challenges
  15. Deployment and Testing
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Workflow of the proposed methodology.