Automatic Generation of Acceptance Test Cases From Use Case Specifications: An NLP-Based Approach

TLDR

Acceptance testing validates that software meets functional requirements, a critical activity in safety‑critical systems where each requirement must be traceably verified, yet generating test cases from large natural‑language use‑case specifications is costly and error‑prone. The authors propose UMTG to automatically generate executable, system‑level acceptance test cases from natural‑language use‑case specifications, aiming to reduce manual effort and ensure coverage. UMTG automates test case generation by applying NLP to use‑case specifications and a domain model, extracting test scenarios, generating formal constraints for execution conditions, and producing test data without imposing strict expressiveness limits. In two industrial case studies, UMTG correctly translated 95 % of use‑case steps into formal constraints and produced test cases covering all expert‑implemented scenarios plus additional critical ones.

Abstract

Acceptance testing is a validation activity performed to ensure the conformance of software systems with respect to their functional requirements. In safety critical systems, it plays a crucial role since it is enforced by software standards, which mandate that each requirement be validated by such testing in a clearly traceable manner. Test engineers need to identify all the representative test execution scenarios from requirements, determine the runtime conditions that trigger these scenarios, and finally provide the input data that satisfy these conditions. Given that requirements specifications are typically large and often provided in natural language (e.g., use case specifications), the generation of acceptance test cases tends to be expensive and error-prone. In this paper, we present Use Case Modeling for System-level, Acceptance Tests Generation (UMTG), an approach that supports the generation of executable, system-level, acceptance test cases from requirements specifications in natural language, with the goal of reducing the manual effort required to generate test cases and ensuring requirements coverage. More specifically, UMTG automates the generation of acceptance test cases based on use case specifications and a domain model for the system under test, which are commonly produced in many development environments. Unlike existing approaches, it does not impose strong restrictions on the expressiveness of use case specifications. We rely on recent advances in natural language processing to automatically identify test scenarios and to generate formal constraints that capture conditions triggering the execution of the scenarios, thus enabling the generation of test data. In two industrial case studies, UMTG automatically and correctly translated 95 percent of the use case specification steps into formal constraints required for test data generation; furthermore, it generated test cases that exercise not only all the test scenarios manually implemented by experts, but also some critical scenarios not previously considered.