相关论文: A large-scale comparative analysis of Coding Stand…

Refactoring aims at improving code non-functional attributes without modifying its external behavior. Previous studies investigated the motivations behind refactoring by surveying developers. With the aim of generalizing and complementing…

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder.…

Background: Research software plays an important role in solving real-life problems, empowering scientific innovations, and handling emergency situations. Therefore, the correctness and trustworthiness of research software are of absolute…

Data is a cornerstone of empirical software engineering (ESE) research and practice. Data underpin numerous process and project management activities, including the estimation of development effort and the prediction of the likely location…

Keeping up with the research literature plays an important role in the workflow of scientists - allowing them to understand a field, formulate the problems they focus on, and develop the solutions that they contribute, which in turn shape…

Recent studies have demonstrated remarkable advancements in source code learning, which applies deep neural networks (DNNs) to tackle various software engineering tasks. Similar to other DNN-based domains, source code learning also requires…

With the boom in modern software development, open-source software has become an integral part of various industries, driving progress in computer science. However, the immense complexity and diversity of the open-source ecosystem also pose…

Engineering software systems is a multidisciplinary activity, whereby a number of artifacts must be created - and maintained - synchronously. In this paper we investigate whether production code and the accompanying tests co-evolve by…

Background: Assessing and communicating software engineering research can be challenging. Design science is recognized as an appropriate research paradigm for applied research but is seldom referred to in software engineering. Applying the…

Context: Software systems are in continuous evolution through source code changes to fixing bugs, adding new functionalities and improving the internal architecture. All these practices are recorded in the version history, which can be…

[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold for Machine Learning (ML) projects, which involve…

Reading code is an essential activity in software maintenance and evolution. Several studies with human subjects have investigated how different factors, such as the employed programming constructs and naming conventions, can impact code…

Recently, workforce shortage has become a popular issue in information technology (IT). One solution to increasing the workforce supply is to increase the number of female IT professionals. This is because there is gender imbalance in…

There has been growing interest within the computational science and engineering (CSE) community in engaging with software engineering research -- the systematic study of software systems and their development, operation, and maintenance --…

There is abundant observational data in the software engineering domain, whereas running large-scale controlled experiments is often practically impossible. Thus, most empirical studies can only report statistical correlations -- instead of…

In software development, the identification of source code file experts is an important task. Identifying these experts helps to improve software maintenance and evolution activities, such as developing new features, code reviews, and bug…

The proliferation of open-source scientific software for science and research presents opportunities and challenges. In this paper, we introduce the SciCat dataset -- a comprehensive collection of Free-Libre Open Source Software (FLOSS)…

Quantitative research relies heavily on coding, and coding errors are relatively common even in published research. In this paper, we examine whether individuals are more or less likely to check their code depending on the results they…

Scientists across disciplines write code for critical activities like data collection and generation, statistical modeling, and visualization. As large language models that can generate code have become widely available, scientists may…

Be it in debugging, testing, code review or, more recently, pair programming with AI assistance: in all these activities, software engineers need to understand source code. Accordingly, plenty of research is taking place in the field to…