In today's organizations, software is mission-critical. However, the legacy of past decisions can make tasks related to artifacts increasingly inefficient or risky, creating debt. While most researchers and practitioners mainly focus on technical debt, some have investigated its social dimensions, known as social debt. We argue that organizations developing software need to tackle debt holistically, as it is intrinsically a socio-technical issue. In this short paper, we rely on a definition of socio-technical debt based on the existing literature to define Debt Stories: a tool based on the User Story format, that can help capture debt elements directly from the stakeholders involved in software development. A debt story includes information about the role of the stakeholder in the development process, the social or technical context, and the impact of the debt element on the different tasks performed by the stakeholder. We provide a first empirical evaluation of the usage of Debt Stories in an industrial context, demonstrating the relevance of Debt Stories to express and communicate socio-technical debt.

Conducting socio-technical software engineering research on closed-source software is difficult as most organizations do not want to give access to their code repositories. Most experiments and publications therefore focus on open-source projects, which only provides a partial view of software development communities. Yet, closing the gap between open and closed source software industries is essential to increase the validity and applicability of results stemming from socio-technical software engineering research. We contribute to this effort by sharing our work in a large company counting 4,800 employees. We mined 101 repositories and produced the GDED dataset containing socio-technical information about 106,216 commits, 470,940 file modifications and 3,471,556 method modifications from 164 developers during the last 13 years, using various programming languages. For that, we used GitDelver, an open-source tool we developed on top of Pydriller, and anonymized and scrambled the data to comply with legal and corporate requirements. Our dataset can be used for various purposes and provides information about code complexity, self-admitted technical debt, bug fixes, as well as temporal information. We also share our experience regarding the processing of sensitive data to help other organizations making datasets publicly available to the research community.