Byzantine fault tolerant algorithms are critical for achieving consistency and reliability in distributed systems, especially in the presence of faults or adversarial behavior. The consensus algorithm used by the XRP Ledger falls into this category. In practice, the implementation of these algorithms is prone to errors, which can lead to undesired behavior in the system. This paper introduces Rocket, a fuzz-testing framework designed for the XRPL consensus algorithm. Rocket enables researchers and developers to automatically inject network and process faults into a locally simulated network of XRPL validator nodes to test if the system behaves as expected. This technique has previously been shown to be effective in finding implementation errors. Rocket has been designed to focus on extensibility and ease of use, enabling users to run complex test scenarios with minimal setup. Video: https://www.youtube.com/watch?v=07Z3ufRa51Y

Software testing is critical for ensuring the quality of software systems. Manually writing test cases is time-intensive and costly, which has led to the development of automated test case generation techniques. However, the adoption of these techniques is limited due to the difficulties in comprehending the generated test cases. In this paper, we propose an approach to improve the comprehension of automatically generated test suites by clustering the test cases within the test suite. Our approach clusters the test cases based on the test objectives (e.g., lines and branches) they cover, grouping together those with similar attributes to enhance developer understanding. To evaluate our approach, we conducted an empirical study with 52 participants performing three software maintenance tasks based on related work. The results show developers agree with the proposed clusters and that clustered test suites facilitate faster software maintenance tasks.

Transaction-reverting statements are key constructs within Solidity that are extensively used for authority and validity checks. Current state-of-the-art search-based testing and fuzzing approaches do not explicitly handle these statements and therefore can not effectively detect security vulnerabilities. In this paper, we argue that it is critical to directly handle and test these statements to assess that they correctly protect the contracts against invalid requests. To this aim, we propose a new approach that improves the search guidance for these transaction-reverting statements based on interprocedural control dependency analysis, in addition to the traditional coverage criteria. We assess the benefits of our approach by performing an empirical study on 100 smart contracts w.r.t. transaction-reverting statement coverage and vulnerability detection capability. Our results show that the proposed approach can improve the performance of Dy-naMOSA, the state-of-the-art algorithm for test case generation. On average, we improve transaction-reverting statement coverage by 14 % (up to 35 %), line coverage by 8 % (up to 32 %), and vulnerability-detection capability by 17 % (up to 50 %).

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.

Software testing is an important and time-consuming task that is often done manually. In the last decades, researchers have come up with techniques to generate input data (e.g., fuzzing) and automate the process of generating test cases (e.g., search-based testing). However, these techniques are known to have their own limitations: search-based testing does not generate highly-structured data; grammar-based fuzzing does not generate test case structures. To address these limitations, we combine these two techniques. By applying grammar-based mutations to the input data gathered by the search-based testing algorithm, it allows us to co-evolve both aspects of test case generation. We evaluate our approach, called G-EvoSuite, by performing an empirical study on 20 Java classes from the three most popular JSON parsers across multiple search budgets. Our results show that the proposed approach on average improves branch coverage for JSON related classes by 15 % (with a maximum increase of 50 %) without negatively impacting other classes.