Generalizing Test Cases for Comprehensive Test Scenario Coverage

2026-04-23

AI Summary

Existing automated test generation approaches heavily rely on code coverage metrics, often failing to capture diverse test scenarios driven by implicit requirements and thereby risking the omission of critical defects. To address this limitation, this work proposes TestGeneralizer, a novel framework that treats an initial test as an executable specification. By leveraging large language models to infer its underlying requirements, TestGeneralizer constructs reusable test scenario templates and systematically instantiates them, enabling generalization from a single test to a comprehensive set of scenarios. This paradigm shifts beyond traditional coverage-driven methods, and empirical evaluation on twelve open-source Java projects demonstrates that TestGeneralizer improves mutation detection rate by 31.66% and LLM-assessed scenario coverage by 23.08% compared to ChatTester.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Test cases are essential for software development and maintenance. In practice, developers derive multiple test cases from an implicit pattern based on their understanding of requirements and inference of diverse test scenarios, each validating a specific behavior of the focal method. However, producing comprehensive tests is time-consuming and error-prone: many important tests that should have accompanied the initial test are added only after a significant delay, sometimes only after bugs are triggered. Existing automated test generation techniques largely focus on code coverage. Yet in real projects, practical tests are seldom driven by code coverage alone, since test scenarios do not necessarily align with control-flow branches. Instead, test scenarios originate from requirements, which are often undocumented and implicitly embedded in a project's design and implementation. However, developer-written tests are frequently treated as executable specifications; thus, even a single initial test that reflects the developer's intent can reveal the underlying requirement and the diverse scenarios that should be validated. In this work, we propose TestGeneralizer, a framework for generalizing test cases to comprehensively cover test scenarios. TestGeneralizer orchestrates three stages: (1) enhancing the understanding of the requirement and scenario behind the focal method and initial test; (2) generating a test scenario template and crystallizing it into various test scenario instances; and (3) generating and refining executable test cases from these instances. We evaluate TestGeneralizer against three state-of-the-art baselines on 12 open-source Java projects. TestGeneralizer achieves significant improvements: +31.66% and +23.08% over ChatTester, in mutation-based and LLM-assessed scenario coverage, respectively.

Problem

• test case generalization
• test scenario coverage
• software testing
• requirement inference
• executable specification

Innovation

• test case generalization
• test scenario coverage
• requirement inference
• automated test generation
• executable specification