In modern software engineering, build systems play the crucial role of facilitating the conversion of source code into software artifacts. Recent research has explored high-level causes of build failures, but has largely overlooked the structural properties of build files. Akin to source code, build systems face technical debt challenges that hinder maintenance and optimization. While refactoring is often seen as a key tool for addressing technical debt in source code, there is a significant research gap regarding the specific refactoring changes developers apply to build code and whether these refactorings effectively address technical debt.In this paper, we address this gap by examining refactorings applied to build scripts in open-source projects, covering the widely used build systems of Gradle, Ant, and Maven. Additionally, we investigate whether these refactorings are used to tackle technical debts in build systems. Our analysis was conducted on 725 examined build-file-related commits. We identified 24 build-related refactorings, which we divided into 6 main categories. These refactorings are organized into the first empirically derived taxonomy of build system refactorings. Furthermore, we investigate how developers employ these refactoring types to address technical debts via a manual commitanalysis and a developer survey. In this context, we identified 5 technical debts addressed by these refactorings and discussed their correlation with the different refactorings. Finally, we introduce BuildRefMiner, an LLM-powered tool leveraging GPT40 to automate the detection of refactorings within build systems. We evaluated its performance and found that it achieves an F1 score of 0.76 across all build systems.This study will serve as a foundational building block for guiding future research and practice in the maintenance and optimization of build systems. BuildRefMiner and the replication package for this study are available at [1]

Software containerization using Docker has recently become the de facto standard for delivering reusable software artifacts. Integral to Docker's functionality are Dockerfiles, which serve as scripts that define the layers and components to be incorporated within a container. Although these files serve as the bedrock of container creation, their maintenance presents intricate challenges. Specifically, the task of Dockerfile refactoring is compounded by its inherent complexity. Although the importance of refactoring inside Docker ecosystems is apparent, detecting it remains challenging. Developers usually avoid documenting their refactoring efforts, often combining them with other changes.

Code quality aspects such as code smells and code quality metrics are widely used in exploratory and empirical software engineering research. In such studies, researchers spend a substantial amount of time and effort to not only select the appropriate subject systems but also to analyze them to collect the required code quality information. In this paper, we present QScored dataset; the dataset contains code quality information of more than 86 thousand C# and Java GitHub repositories containing more than 1.1 billion lines of code. The code quality information contains seven kinds of detected architecture smells, 20 kinds of design smells, eleven kinds of implementation smells, and 27 commonly used code quality metrics computed at project, package, class, and method levels. Availability of the dataset will facilitate empirical studies involving code quality aspects by making the information readily available for a large number of active GitHub repositories.