Requirements Coverage-Guided Minimization for Natural Language Test Cases

TL;DR

This paper introduces RTM, a novel requirement coverage-guided test suite minimization approach for natural language test cases, effectively reducing redundancy while maintaining coverage and fault detection in safety-critical systems.

Contribution

RTM is a new method that combines text preprocessing, embedding, similarity computation, and genetic algorithms to optimize requirement-based test suites.

Findings

RTM outperforms baseline techniques in fault detection rate across budgets.

RTM maintains full requirement coverage after minimization.

Test suite redundancy impacts the effectiveness of minimization.

Abstract

As software systems evolve, test suites tend to grow in size and often contain redundant test cases. Such redundancy increases testing effort, time, and cost. Test suite minimization (TSM) aims to eliminate such redundancy while preserving key properties such as requirement coverage and fault detection capability. In this paper, we propose RTM (Requirement coverage-guided Test suite Minimization), a novel TSM approach designed for requirement-based testing (validation), which can effectively reduce test suite redundancy while ensuring full requirement coverage and a high fault detection rate (FDR) under a fixed minimization budget. Based on common practice in critical systems where functional safety is important, we assume test cases are specified in natural language and traced to requirements before being implemented. RTM preprocesses test cases using three different preprocessing methods, and then converts them into vector representations using seven text embedding techniques. Similarity values between vectors are computed utilizing three distance functions. A Genetic Algorithm, whose population is initialized by coverage-preserving initialization strategies, is then employed to identify an optimized subset containing diverse test cases matching the set budget. We evaluate RTM on an industrial automotive system dataset comprising $736$ system test cases and $54$ requirements. Experimental results show that RTM consistently outperforms baseline techniques in terms of FDR across different minimization budgets while maintaining full requirement coverage. Furthermore, we investigate the impact of test suite redundancy levels on the effectiveness of TSM, providing new insights into optimizing requirement-based test suites under practical constraints.