Christoph Laaber

Erblin Isaku, Christoph Laaber, Hassan Sartaj et al.

The Cancer Registry of Norway (CRN) uses an automated cancer registration support system (CaReSS) to support core cancer registry activities, i.e, data capture, data curation, and producing data products and statistics for various stakeholders. GURI is a core component of CaReSS, which is responsible for validating incoming data with medical rules. Such medical rules are manually implemented by medical experts based on medical standards, regulations, and research. Since large language models (LLMs) have been trained on a large amount of public information, including these documents, they can be employed to generate tests for GURI. Thus, we propose an LLM-based test generation and differential testing approach (LLMeDiff) to test GURI. We experimented with four different LLMs, two medical rule engine implementations, and 58 real medical rules to investigate the hallucination, success, time efficiency, and robustness of the LLMs to generate tests, and these tests' ability to find potential issues in GURI. Our results showed that GPT-3.5 hallucinates the least, is the most successful, and is generally the most robust; however, it has the worst time efficiency. Our differential testing revealed 22 medical rules where implementation inconsistencies were discovered (e.g., regarding handling rule versions). Finally, we provide insights for practitioners and researchers based on the results.

Giovanni Grano, Christoph Laaber, Annibale Panichella et al.

Automated test case generation is an effective technique to yield high-coverage test suites. While the majority of research effort has been devoted to satisfying coverage criteria, a recent trend emerged towards optimizing other non-coverage aspects. In this regard, runtime and memory usage are two essential dimensions: less expensive tests reduce the resource demands for the generation process and later regression testing phases. This study shows that performance-aware test case generation requires solving two main challenges: providing a good approximation of resource usage with minimal overhead and avoiding detrimental effects on both final coverage and fault detection effectiveness. To tackle these challenges, we conceived a set of performance proxies -- inspired by previous work on performance testing -- that provide a reasonable estimation of the test execution costs (i.e., runtime and memory usage). Thus, we propose an adaptive strategy, called aDynaMOSA, which leverages these proxies by extending DynaMOSA, a state-of-the-art evolutionary algorithm in unit testing. Our empirical study -- involving 110 non-trivial Java classes -- reveals that our adaptive approach generates test suite with statistically significant improvements in runtime (-25%) and heap memory consumption (-15%) compared to DynaMOSA. Additionally, aDynaMOSA has comparable results to DynaMOSA over seven different coverage criteria and similar fault detection effectiveness. Our empirical investigation also highlights that the usage of performance proxies (i.e., without the adaptiveness) is not sufficient to generate more performant test cases without compromising the overall coverage.