AI-Assisted Unit Test Writing and Test-Driven Code Refactoring: A Case Study

• URL: http://arxiv.org/abs/2604.03135v1
• Date: Fri, 03 Apr 2026 15:54:43 GMT
• Title: AI-Assisted Unit Test Writing and Test-Driven Code Refactoring: A Case Study
• Abstract summary: We present a case study of using coding models for automated unit test generation and subsequent safe iteration.<n>We generated nearly 16,000 lines of reliable unit tests in hours rather than weeks, achieved up to 78% branch coverage in critical modules, and significantly reduced regression risk during large-scale iterations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many software systems originate as prototypes or minimum viable products (MVPs), developed with an emphasis on delivery speed and responsiveness to changing requirements rather than long-term code maintainability. While effective for rapid delivery, this approach can result in codebases that are difficult to modify, presenting a significant opportunity cost in the era of AI-assisted or even AI-led programming. In this paper, we present a case study of using coding models for automated unit test generation and subsequent safe refactoring, with proposed code changes validated by passing tests. The study examines best practices for iteratively generating tests to capture existing system behavior, followed by model-assisted refactoring under developer supervision. We describe how this workflow constrained refactoring changes, the errors and limitations observed in both phases, the efficiency gains achieved, when manual intervention was necessary, and how we addressed the weak value misalignment we observed in models. Using this approach, we generated nearly 16,000 lines of reliable unit tests in hours rather than weeks, achieved up to 78\% branch coverage in critical modules, and significantly reduced regression risk during large-scale refactoring. These results illustrate software engineering's shift toward an empirical science, emphasizing data collection and constraining mechanisms that support fast, safe iteration.

Related papers

• When Do Intrinsic Rewards Work for Code Reasoning? A Comprehensive Study [77.64957118012423]
  Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in large language model reasoning.<n>Recent work addresses this by deriving rewards from a model's own signals, such as majority voting or confidence-based scores.<n>We present a systematic empirical study of intrinsic reward methods for code generation.
  arXiv  Detail & Related papers  (2026-06-18T19:15:50Z)
• BACE: LLM-based Code Generation through Bayesian Anchored Co-Evolution of Code and Test Populations [0.9668407688201359]
  We introduce BACE, a framework that reformulates synthesis as a Bayesian co-evolutionary process.<n>BACE achieves superior performance across both proprietary models and open-weight small language models.
  arXiv  Detail & Related papers  (2026-03-30T16:40:11Z)
• Taming Imperfect Process Verifiers: A Sampling Perspective on Backtracking [54.43083499412643]
  Test-time algorithms that combine the generative power of language models with process verifiers offer a promising lever for eliciting new reasoning capabilities.<n>We introduce a new process-guided test-time sampling algorithm, VGB, which uses theoretically grounded backtracking to achieve provably better robustness to verifier errors.
  arXiv  Detail & Related papers  (2025-10-03T16:21:14Z)
• Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models [0.0]
  Generative AI is gaining increasing attention in software engineering.<n>Unit tests constitute the majority of test cases and are often schematic.<n>This paper investigates the impact of code context and prompting strategies on the quality and adequacy of unit tests.
  arXiv  Detail & Related papers  (2025-07-18T11:23:17Z)
• Training Language Models to Generate Quality Code with Program Analysis Feedback [66.0854002147103]
  Code generation with large language models (LLMs) is increasingly adopted in production but fails to ensure code quality.<n>We propose REAL, a reinforcement learning framework that incentivizes LLMs to generate production-quality code.
  arXiv  Detail & Related papers  (2025-05-28T17:57:47Z)
• MANTRA: Enhancing Automated Method-Level Refactoring with Contextual RAG and Multi-Agent LLM Collaboration [44.75848695076576]
  We introduce MANTRA, a comprehensive Large Language Models agent-based framework.<n>ManTRA integrates Context-Aware Retrieval-Augmented Generation, coordinated Multi-Agent Collaboration, and Verbal Reinforcement Learning.<n> Experimental results demonstrate that MANTRA substantially surpasses a baseline LLM model.
  arXiv  Detail & Related papers  (2025-03-18T15:16:51Z)
• Learning to Solve and Verify: A Self-Play Framework for Code and Test Generation [69.62857948698436]
  Recent advances in large language models (LLMs) have improved their performance on coding benchmarks.<n>However, improvement is plateauing due to the exhaustion of readily available high-quality data.<n>We propose Sol-Ver, a self-play solver-verifier framework that jointly improves a single model's code and test generation capacity.
  arXiv  Detail & Related papers  (2025-02-20T18:32:19Z)
• Generating refactored code accurately using reinforcement learning [3.179831861897336]
  We propose a novel reinforcement learning-based approach for fine-tuning and aligning code language models to perform automated, intelligent extract method on Java source code.<n>Our approach fine-tunes sequence-to-sequence generative models and aligns them using the Proximal Policy Optimization (PPO) algorithm.<n>Our experiments demonstrate that our approach significantly enhances the performance of large language models in code.
  arXiv  Detail & Related papers  (2024-12-23T23:09:48Z)
• Leveraging Large Language Models for Efficient Failure Analysis in Game Development [47.618236610219554]
  This paper proposes a new approach to automatically identify which change in the code caused a test to fail. The method leverages Large Language Models (LLMs) to associate error messages with the corresponding code changes causing the failure. Our approach reaches an accuracy of 71% in our newly created dataset, which comprises issues reported by developers at EA over a period of one year.
  arXiv  Detail & Related papers  (2024-06-11T09:21:50Z)
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning [92.36705236706678]
  "CodeRL" is a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning. During inference, we introduce a new generation procedure with a critical sampling strategy. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives.
  arXiv  Detail & Related papers  (2022-07-05T02:42:15Z)

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.