Abstract

Abstract

Abstract
Validation is a central activity when developing formal specifications. Similarly to coding, a possible validation technique is to define upfront test cases or scenarios that a future specification should satisfy or not. Unfortunately, specifying such test cases is burdensome and error prone, which could cause users to skip this validation task. This paper reports the results of an empirical evaluation of using pre-trained large language models (LLMs) to automate the generation of test cases from natural language requirements. In particular, we focus on test cases for structural requirements of simple domain models formalized in the Alloy specification language. Our evaluation focuses on the state-of-theart GPT-5 model, but results from other closed- and open-source LLMs are also reported. The results show that, in this context, GPT-5 is already quite effective at generating positive (and negative) test cases that are syntactically correct and that satisfy (or not) the given requirement, and that can detect many wrong specifications written by humans.

Abstract
Analyzing time series of counts often encounters the challenge of missing data, which can significantly hinder the accuracy and reliability of statistical models. This study addresses this issue by employing Poisson first-order integer-valued au-toregressive (PoINAR) models in conjunction with the Gibbs sampler with data augmentation. This method is particularly effective as it accounts for both the mechanisms behind missing data and the intrinsic serial correlation within the time series. Two distinct approaches to data augmentation are explored and compared in this work and illustrated using both simulated and real data.

Abstract

Abstract
Atlantic salmon populations have declined in many regions and are affected by several natural and anthropogenic factors throughout their lives. We investigated the role of environmental drivers and the effect of dam construction on the trend in catches of spawning adults of a migratory population currently at risk. For this purpose, we examined the salmon catches from 1914 to 2020 in the Minho River (NW Portugal, SW Europe), located at the southern limit of this species' distribution. There was a decline in catches over time with an inverse and significant relationship between the trend in catches and lagged temperature. Delayed effects of this type may indicate temperature influences on survival during early life history stages. Similarly, the trend in catches decreased with the increasing number of dams. A forecast model built for the period before the construction of the first major dam in this river (before 1955), including lagged temperature, resulted in a decreasing trend in the number of catches. This demonstrates that catches would have declined due to temperature effects even without dam construction. This does not diminish the role of dams in the observed decline; rather, it reveals that temperature-driven declines would have occurred independently. Nonetheless, efficient management and conservation of this imperiled population require further detailed biological information on the number of returning spawning adults and salmons' survival throughout their life cycle.