Abstract:Research on automating software testing has spanned several decades. Most existing approaches generate unit tests for individual methods, validate isolated API endpoints, or target user interface (UI) layers, with non-API and non-UI generators typically exercising only a single focal method. Recent empirical evidence shows a substantial gap between such generated tests and developer-written ones, which often span several focal classes and methods, involve multi-step call sequences, and contain chained assertions, all characteristics that current approaches fail to capture. To address this gap, we propose generating tests from natural language (NL) descriptions of developer intent, an expressive and accessible medium for specifying complex test scenarios. We present Sakura, the first agent-based framework for generating structurally complex tests from NL descriptions. Sakura decomposes NL descriptions into structured blocks and processes them with a multi-agent system: a localization agent grounds test steps in concrete application code via static analysis, a composition agent synthesizes compilable test code and iteratively refines it using execution feedback, and a supervisor agent coordinates their interactions. To evaluate Sakura, we curate a novel dataset of NL test descriptions at three levels of abstraction, reflecting different end-user personas, systematically derived from developer-written tests in Apache Commons projects. Across 20 applications and 1,464 test scenarios, Sakura substantially outperforms off-the-shelf agentic tools such as Gemini CLI instantiated with multiple LLMs, achieving 50-78% higher test compilability and 38-66% higher coverage overlap with ground-truth tests using the same models. Moreover, Sakura paired with small open-source models such as Devstral Small 2 and Qwen3-Coder outperforms Gemini CLI using large proprietary models, at lower cost.
From: Tyler Stennett [view email]
[v1]
Sat, 30 May 2026 04:49:10 UTC (2,895 KB)
[v2]
Sun, 2 Aug 2026 19:01:22 UTC (1,672 KB)
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