Mitchell Olsthoorn

Mitchell Olsthoorn, Pouria Derakhshanfar, Annibale Panichella

State-of-the-art search-based approaches for test case generation work at test case level, where tests are represented as sequences of statements. These approaches make use of genetic operators (i.e., mutation and crossover) that create test variants by adding, altering, and removing statements from existing tests. While this encoding schema has been shown to be very effective for many-objective test case generation, the standard crossover operator (single-point) only alters the structure of the test cases but not the input data. In this paper, we argue that changing both the test case structure and the input data is necessary to increase the genetic variation and improve the search process. Hence, we propose a hybrid multi-level crossover (HMX) operator that combines the traditional test-level crossover with data-level recombination. The former evolves and alters the test case structures, while the latter evolves the input data using numeric and string-based recombinational operators. We evaluate our new crossover operator by performing an empirical study on more than 100 classes selected from open-source Java libraries for numerical operations and string manipulation. We compare HMX with the single-point crossover that is used in EvoSuite w.r.t structural coverage and fault detection capability. Our results show that HMX achieves a statistically significant increase in 30% of the classes up to 19% in structural coverage compared to the single-point crossover. Moreover, the fault detection capability improved up to 12% measured using strong mutation score.

Ali Asgari, Mitchell Olsthoorn, Annibale Panichella

Recently, test case selection (TCS) techniques have been explored to support the operational evaluation of deep neural networks (DNNs) under limited testing budgets, where labeling cost is a primary concern and uncovering model failures early is a key objective. Although prior studies report promising results, existing empirical evaluations focus almost exclusively on vision-based DNNs and datasets, leaving it unclear whether prior findings generalize to LLM code models. This paper presents a large-scale replication study of TCS techniques in the context of LLM code models. We re-examine established TCS strategies originally proposed for DNNs and complement them with statistical sampling strategies not previously evaluated for TCS. We assess their effectiveness on three code-related classification tasks: clone detection, vulnerability detection, and technical debt prediction. The study spans 17 task-specific fine-tuned model instances, 7 predictive features, and 13 selection strategies, including 12 feature-aware strategies and simple random sampling (SRS) as a feature-agnostic baseline. We evaluate performance along two dimensions: accuracy estimation and early failure discovery. The results indicate that only a subset of findings reported for vision-based DNNs generalize when TCS is applied to LLMs for code. In particular, uncertainty-based features are effective for early failure discovery, while representation-based features are more robust for accuracy estimation. At the same time, performance varies substantially across tasks and models, indicating that TCS effectiveness is context-dependent. Overall, this study provides empirical evidence on the replicability of TCS techniques beyond vision-based deep learning and offers insights into their use for the operational evaluation of LLMs for code.

Dimitri Stallenberg, Mitchell Olsthoorn, Annibale Panichella

With the ever-increasing use of web APIs in modern-day applications, it is becoming more important to test the system as a whole. In the last decade, tools and approaches have been proposed to automate the creation of system-level test cases for these APIs using evolutionary algorithms (EAs). One of the limiting factors of EAs is that the genetic operators (crossover and mutation) are fully randomized, potentially breaking promising patterns in the sequences of API requests discovered during the search. Breaking these patterns has a negative impact on the effectiveness of the test case generation process. To address this limitation, this paper proposes a new approach that uses agglomerative hierarchical clustering (AHC) to infer a linkage tree model, which captures, replicates, and preserves these patterns in new test cases. We evaluate our approach, called LT-MOSA, by performing an empirical study on 7 real-world benchmark applications w.r.t. branch coverage and real-fault detection capability. We also compare LT-MOSA with the two existing state-of-the-art white-box techniques (MIO, MOSA) for REST API testing. Our results show that LT-MOSA achieves a statistically significant increase in test target coverage (i.e., lines and branches) compared to MIO and MOSA in 4 and 5 out of 7 applications, respectively. Furthermore, LT-MOSA discovers 27 and 18 unique real-faults that are left undetected by MIO and MOSA, respectively.

Mitchell Olsthoorn, Annibale Panichella

Test Case Selection (TCS) aims to select a subset of the test suite to run for regression testing. The selection is typically based on past coverage and execution cost data. Researchers have successfully used multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and its variants, to solve this problem. These MOEAs use traditional crossover operators to create new candidate solutions through genetic recombination. Recent studies in numerical optimization have shown that better recombinations can be made using machine learning, in particular link-age learning. Inspired by these recent advances in this field, we propose a new variant of NSGA-II, called L2-NSGA, that uses linkage learning to optimize test case selection. In particular, we use an unsupervised clustering algorithm to infer promising patterns among the solutions (subset of test suites). Then, these patterns are used in the next iterations of L2-NSGA to create solutions that preserve these inferred patterns. Our results show that our customizations make NSGA-II more effective for test case selection. The test suite sub-sets generated by L2-NSGA are less expensive and detect more faults than those generated by MOEAs used in the literature for regression testing.