Abstract
Python has become one of the most widely used programming languages, yet the transition fromPython 2 to 3 introduced a tension between innovation and compatibility. While new featuressuch as formatted string literals, type annotations, and structural pattern matching expanded thelanguage’s expressiveness, they also required substantial adaptation of legacy code. Despite theincreasing relevance of these features, there is still limited empirical evidence on how modernPython features are being adopted in practice—when developers start using them, how adoptionunfolds over time, and what motivations drive these decisions. This paper addresses this gapthrough a large-scale empirical study of 424 open-source Python projects. Our analysis revealstwo distinct adoption patterns: rapid adoption of small syntactic improvements and slowerintegration of features that require extensive refactoring or ecosystem support. On average,projects begin using with new features within 16 months after their release but take roughly 4years to achieve broader and sustained adoption. This observation may be partially explainedby the transition from Python 2 to 3, which did not preserve full backward compatibility.Complementary qualitative evidence from pull-request discussions indicates that developers areprimarily motivated to rejuvenate Python code through improvements in comprehension, safety,and performance, yet often constrained by compatibility requirements and maintenance costs.Together, these findings offer practical insights for tool developers and maintainers seeking tobalance innovation and stability in the ongoing rejuvenation of Python source code.

Abstract
Software testing is an integral part of modern software development. Testing frameworks are part of the toolset of any software language allowing programmers to test their programs in order to detect bugs. Unfortunately, there is no work on testing in attribute grammars. In this paper we combine the powerful property-based testing technique with the attribute grammar formalism. In such property-based attribute grammars, properties are defined on attribute instances. Properties are tested on large sets of randomly generated (abstract syntax) trees by evaluating their attributes. We present an implementation that relies on strategies to express property-based attribute grammars. Strategies are tree-based recursion patterns that are used to encode logic quantifiers defining the properties.

Abstract

Abstract
The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions. The ''Greening AI with Software Engineering'' workshop,1 funded by the Centre Europ´een de Calcul Atomique et Mol´eculaire (CECAM) and the Lorentz Center, provided an interdisciplinary forum for 29 participants, from practitioners to academics, to share knowledge, ideas, practices, and current results dedicated to advancing green software and AI research. The workshop was held February 3-7, 2025, in Lausanne, Switzerland. Through keynotes, flash talks, and collaborative discussions, participants identified and prioritized key challenges for the field. These included energy assessment and standardization, benchmarking practices, sustainability-aware architectures, runtime adaptation, empirical methodologies, and education. This report presents a research agenda emerging from the workshop, outlining open research directions and practical recommendations to guide the development of environmentally sustainable AI-enabled systems rooted in software engineering principles.

Abstract
JavaScript is a widely used programming language initially designed to make the Web more dynamic in the 1990s. In the last decade, though, its scope has extended far beyond the Web, finding utility in backend development, desktop applications, and even IoT devices. To circumvent the needs of modern programming, JavaScript has undergone a remarkable evolution since its inception, with the groundbreaking release of its sixth version in 2015 (ECMAScript 6 standard). While adopting modern JavaScript features promises several benefits (such as improved code comprehension and maintenance), little is known about which modern features of the language have been used in practice (or even ignored by the community). To fill this gap, in this paper, we report the results of an empirical study that aims to understand the adoption trends of modern JavaScript features, and whether or not developers conduct rejuvenation efforts to replace legacy JavaScript constructs and idioms with modern ones in legacy systems. To this end, we mined the source code history of 158 JavaScript open-source projects, identified contributions to rejuvenate legacy code, and used time series to characterize the adoption trends of modern JavaScript features. The results of our study reveal extensive use of JavaScript modern features which are present in more than 80% of the analyzed projects. Our findings also reveal that (a) the widespread adoption of modern features happened between one and two years after the release of ES6 and, (b) a consistent trend toward increasing the adoption of modern JavaScript language features in open-source projects and (c) large efforts to rejuvenate the source code of their programs.