Background: Systematic literature reviews (SLRs) require robust search strategies to ensure comprehensive coverage. Although database searches have traditionally been the primary method, snowballing has emerged as an effective alternative strategy in software engineering research. However, the success of snowballing heavily depends on the initial seed set's composition, particularly regarding diversity across authors, publication years, and venues. Objective: This study investigates how different diversity characteristics in seed set creation influence snowballing performance and effectiveness in identifying relevant literature. Method: We conducted replication studies of two existing SLRs, comparing their conventional seed set creation approaches with our diversity-driven methodology, where we systematically incorporated diversity characteristics into constructing the seed sets. Results: Our diversity-based approach demonstrated substantial improvements, with a precision of 0.019 (compared to 0.006 in the original), a relative recall of 0.97 (versus 0.921), and an F-measure of 0.0372 (improving from 0.0119). Conclusions: The empirical evidence suggests that incorporating diversity criteria in seed set creation enhances snowballing efficacy while maintaining comprehensive coverage of relevant literature. This approach offers a systematic and effective method for conducting snowballbased literature reviews in software engineering research.

Symmetries express the invariance of a system towards sets of mathematical transformations. In more practical terms, symmetries greatly reduce or simplify the computational efforts required to evaluate relevant properties of a system. In this paper, two methods are proposed to implement spin symmetries which simplify the analysis of the spreading of diseases in an agent-based epidemic model. We perform a set of simulations to measure the efficiency gains compared to traditional methods. Our findings show symmetry-based algorithms improve the performance of the Monte Carlo simulation and the exact Markov process.

Summary Software testing is regarded as an important method for fault revealing. Despite this advantage, it has been poorly used within the scope of virtual reality (VR) applications because they are highly complex and have peculiar features. Most testing performed of this VR applications are usability, which is conducted manually and only at final of the development process. Although some works try to propose criteria for this domain, there are no approaches that automatize the generation of test data from requirements specification in the VR domain. This paper proposes an approach called virtual reality—requirements specification and testing (VR‐ReST) to assist the requirements specification through a semiformal language and uses structural test criteria to generate test requirements and test data automatically for VR applications using scene graph concepts. The paper also examines the empirical results concerning the cost‐effectiveness of the approach for three different VR applications through two experiments. Mutation testing was used to evaluate effectiveness. We found that the approach achieved a high mutation score outperforming random testing, by 20%, on average. Our results also demonstrate that the approach is promising since it assists in writing and validating the requirements, as well as in reducing the risks of requirement specification by adopting a semiformal language.