Jul 2026· AIware· pp. 358-366· 1 citation· 26 references
Computer Science
TL;DR
OE25𝑑𝑒𝑣, a multi-variant dataset curated from developer-written unit tests across 25 open-source Java projects spanning 56 modules, and TOGBench, an end-to-end benchmark suite for TOG, which captures six oracle categories and preserves realistic settings, are introduced.
Abstract
Test oracles determine whether a program execution is correct for a given input. Two common forms are assertion oracles, which compare observed outputs with expected results, and exception oracles, which verify that a program raises an expected exception. Automated test oracle generation (TOG) aims to reduce the manual effort involved in constructing such oracles. Although recent TOG methods, especially LLM-based approaches, have made rapid progress, their evaluation remains constrained by benchmarks that rely on automatically generated tests, narrow single-assert formulations, simplified developer-written tests, or limited oracle diversity. To address these limitations, we introduce OE25𝑑𝑒𝑣 , a multi-variant dataset curated from developer-written unit tests across 25 open-source Java projects spanning 56 modules, and TOGBench, an end-to-end benchmark suite for TOG. OE25𝑑𝑒𝑣 captures six oracle categories and preserves realistic settings, including single- and multi-oracle configurations, mixed assertion-and-exception oracles, and developer-authored custom oracles. TOGBench supports end-to-end experimentation by reintegrating generated oracles into runnable test suites and evaluating them via compilation, execution, false-positive analysis, and mutation testing. Our evaluation further shows that OE25𝑑𝑒𝑣 preserves substantially greater structural complexity than prior benchmarks and exposes marked performance degradation of representative TOG models on developer-written tests, particularly for assertion oracles.
Future TOG systems should be evaluated not only by whether they predict the correct oracle type, but also by whether their predictions are grounded in meaningful exception-triggering evidence, to challenge the assumption that strong exception-oracle accuracy reflects robust use of exception semantics.
Soneya Binta Hossain, Matthew B. Dwyer, Tasfia Tasnim· 0 citations
This paper presents a formal mathematical model for categorizing the outcome of generated-tests into four classes, a couple of basic metrics: Bug-Revealing Rate (BRR) and Bug-Validating Rate (BVR); and two basic statistical tests to ensure that the results are rigorous.
Zeyad Farooq Lutfi· Al-Noor Journal of Engineeri...· 0 citations
Test4Py is presented, a novel framework that enhances type correctness in automated test generation for Python by leveraging the program’s call graph to capture richer contextual information about parameters, and introducing a behavior-based type inference mechanism that accurately infers parameter types and constructs...
Runlin Liu, Zhe Zhang, Yunge Hu et al.· ACM Transactions on Software...· 0 citations
MCR-Bench is introduced, the first defect state-aware benchmark designed for realistic multi-round code review, and in-depth error analysis dissects the distinct drivers of false positives and false negatives, revealing critical weaknesses such as cross-round temporal misalignment and inadequate long-range memory.
De-Wu Zheng, Yan-Lin Wang, Xi-Wen Wang et al.· Proceedings of the ACM on So...· 1 citation
An empirical study involving 5 Large Language Models and 4 benchmarks evaluates the effectiveness and efficiency of 3 widely used adequacy criteria: statement coverage, branch coverage, and mutation testing, finding that mutation testing only marginally outperforms traditional coverage criteria in both triggering and d...
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It is found that the usefulness of coverage and mutation is highly context-dependent: in regression-style settings where the code provided to the LLM can be reasonably assumed bug-free, these metrics can provide meaningful signals when comparing across models; in another common scenario where the code-under-test may al...