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To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides,…

Many researchers assume that, for software analytics, "more data is better." We write to show that, at least for learning defect predictors, this may not be true. To demonstrate this, we analyzed hundreds of popular GitHub projects. These…

Scientific software-defined as computer programs, scripts, or code used in scientific research, data analysis, modeling, or simulation-has become central to modern research. However, there is limited research on the readability and…

To bridge the digital skills gap, we need to train more people in Software Engineering techniques. This paper reports on a project exploring the way students solve tasks using collaborative development platforms and version control systems,…

Software is increasingly produced in the form of ecosystems, collections of interdependent components maintained by a distributed community. These ecosystems act as network organizations, not markets, and thus often lack actionable…

Knowledge about software used in scientific investigations is important for several reasons, for instance, to enable an understanding of provenance and methods involved in data handling. However, software is usually not formally cited, but…

With the move towards open research information, the DOI registration agency DataCite is increasingly used as a source for metadata describing research data, for example to perform scientometric analyses. However, there is a lack of…

GitHub repositories consist of various detailed information about the project contributors, the number of commits and its contributors, releases, pull requests, programming languages, and issues. However, no systematic dataset of open…

Much research is done on data analytics and machine learning. In industrial processes large amounts of data are available and many researchers are trying to work with this data. In practical approaches one finds many pitfalls restraining…

Maintaining traceability links between software release notes and corresponding development artifacts, e.g., pull requests (PRs), commits, and issues, is essential for managing technical debt and ensuring maintainability. However, in…

Where can we find malware source code? This question is motivated by a real need: there is a dearth of malware source code, which impedes various types of security research. Our work is driven by the following insight: public archives, like…

Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from many sources, including training data. Existing literature…

Search-engine date filters are widely used to enforce pre-cutoff retrieval in retrospective evaluations of search-augmented forecasters. We show this approach is unreliable across two major search engines: auditing Google Search's before:…

This paper describes the motivation and design of a 10-week graduate course that teaches practices for developing research software; although offered by an engineering program, the content applies broadly to any field of scientific research…

Background: Software development results in the production of various types of artifacts: source code, version control system metadata, bug reports, mailing list conversations, test data, etc. Empirical software engineering (ESE) has…

To ensure the quality of software systems, software engineers can make use of a variety of quality assurance approaches, such as software testing, modern code review, automated static analysis, and build automation. Each of these quality…

Organisations use issue tracking systems (ITSs) to track and document their projects' work in units called issues. This style of documentation encourages evolutionary refinement, as each issue can be independently improved, commented on,…

Software developers create and share code online to demonstrate programming language concepts and programming tasks. Code snippets can be a useful way to explain and demonstrate a programming concept, but may not always be directly…

This paper presents a systematic review of Python packages with a focus on time series analysis. The objective is to provide (1) an overview of the different time series analysis tasks and preprocessing methods implemented, and (2) an…

The work involved in gathering, wrangling, cleaning, and otherwise preparing data for analysis is often the most time consuming and tedious aspect of data work. Although many studies describe data preparation within the context of data…