Related papers: MLDev: Data Science Experiment Automation and Repr…
Obtaining a relevant dataset is central to conducting empirical studies in software engineering. However, in the context of mining software repositories, the lack of appropriate tooling for large scale mining tasks hinders the creation of…
Many researchers have criticized the field of Software Complexity metrics for the lack of testing, verification, and reproducibility of many metrics and case studies that utilized those metrics. This document describes SMF, a tool that can…
The term Model-Driven Engineering (MDE) is typically used to describe software development approaches in which abstract models of software systems are created and systematically transformed to concrete implementations. In this paper we give…
Information technology and data science development stimulate transformation in many fields of scientific knowledge. In recent years, a large number of specialized systems for information and knowledge management have been created in…
We present a longitudinal study on the long-term evolution of maintainability in open-source software. Quality assessment remains at the forefront of both software research and practice, with many models and assessment methodologies…
The Agent Based Model community has a rich and diverse ecosystem of libraries, platforms, and applications to help modelers develop rigorous simulations. Despite this robust and diverse ecosystem, the complexity of life from microbial…
With the advent of open source software, a veritable treasure trove of previously proprietary software development data was made available. This opened the field of empirical software engineering research to anyone in academia. Data that is…
Replicability and reproducibility of experimental results are primary concerns in all the areas of science and IR is not an exception. Besides the problem of moving the field towards more reproducible experimental practices and protocols,…
Mobile app development involves a unique set of challenges including device fragmentation and rapidly evolving platforms, making testing a difficult task. The design space for a comprehensive mobile testing strategy includes features,…
Simulating physical systems is a core component of scientific computing, encompassing a wide range of physical domains and applications. Recently, there has been a surge in data-driven methods to complement traditional numerical simulations…
Whole Tale http://wholetale.org is a web-based, open-source platform for reproducible research supporting the creation, sharing, execution, and verification of "Tales" for the scientific research community. Tales are executable research…
Large Language Models (LLMs) hold promise in automating data analysis tasks, yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios. In this work, we investigate strategies to enhance the data…
Background. Software Engineering (SE) researchers extensively perform experiments with human subjects. Well-defined samples are required to ensure external validity. Samples are selected \textit{purposely} or by \textit{convenience},…
There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers…
Large language models (LLMs) are increasingly used to automate data analysis through executable code generation. Yet, data science tasks often admit multiple statistically valid solutions, e.g. different modeling strategies, making it…
The software development lifecycle depends heavily on the testing process, which is an essential part of finding issues and reviewing the quality of software. Software testing can be done in two ways: manually and automatically. With an…
The performance gap between closed-source and open-source large language models (LLMs) is largely attributed to disparities in access to high-quality training data. To bridge this gap, we introduce a novel framework for the automated…
The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintaining and executing computational saliency models. This work…
Reproducibility and comparability of empirical results are at the core tenet of the scientific method in any scientific field. To ease reproducibility of empirical studies, several benchmarks in software engineering research, such as…
With the increased interest in computational sciences, machine learning (ML), pattern recognition (PR) and big data, governmental agencies, academia and manufacturers are overwhelmed by the constant influx of new algorithms and techniques…