Background: The "Technical Debt Dataset" (TDD) is a comprehensive dataset on technical debt (TD) in the main branches of more than 30 Java projects. However, some TD items produced by Sonar-Qube are not included for many commits, for instance because the commits failed to compile. This has limited previous studies using the dataset. Aims and Method: In this paper, we provide an addition to the dataset that includes an analysis of 278,320 commits of all branches in a superset of 37 projects using Teamscale. We then demonstrate the utility of the dataset by exploring the relationship between developer personality by replicating a prior study. Results: The new dataset allows us to use a larger sample than prior work could, and we analyze the personality of 111 developers and 5,497 of their commits. The relationships we find between developer personality and the introduction and removal of TD differ from those found in prior work. Conclusions: We offer a dataset that may enable future studies into the topic of TD and we provide additional insights on how developer personality relates to TD.

Studies in empirical software engineering are often most useful if they make causal claims because this allows practitioners to identify how they can purposefully influence (rather than only predict) outcomes of interest. Unfortunately, many non-experimental studies suffer from potential endogeneity, for example, through omitted confounding variables, which precludes claims of causality. In this conceptual tutorial, we aim to transfer the proven solution of instrumental variables and two-stage models as a means to account for endogeneity from econometrics to the field of empirical software engineering. To this end, we discuss causality and causal inference, provide a definition of endogeneity, explain its causes, and lay out the conceptual idea behind instrumental variable approaches and two-stage models. We also provide an extensive illustration with simulated data and a brief illustration with real data to demonstrate the approach, offering Stata and R code to allow researchers to replicate our analyses and apply the techniques to their own research projects. We close with concrete recommendations and a guide for researchers on how to deal with endogeneity.

Background: Technical debt (TD) has been widely discussed in software engineering research, and there is an emerging literature linking it to developer characteristics. However, developer personality has not yet been studied in this context. Aims and Method: We explore the relationship between various personality traits (Five Factor Model, regulatory focus, and narcissism) of developers and the introduction and removal of TD. To this end, we complement an existing TD dataset with novel self-report personality data gathered by surveying developers, and analyze 2,145 commits from 19 developers. Results: We find that conscientiousness, emotional stability, openness to experience, and prevention focus are negatively associated with TD. There were no significant results for extraversion, agreeableness, promotion focus, or narcissism. Conclusions: We take our results as first evidence that developer personality has a systematic influence on the introduction and removal of TD. This has implications not only for future research, which could, for example, study the effects of personality on downstream consequences of TD like defects, but also for software engineering practitioners who may, for example, consider developer personality in staffing decisions.

Software engineering capabilities are increasingly important to the success of economic and political blocs. This paper analyzes quantity and quality of software engineering research output originating from the US, Europe, and China over time. The results indicate that the quantity of research is increasing across the board with Europe leading the field. Depending of the scope of the analysis, either the US or China come in second. Regarding research quality, Europe appears to be lagging the other blocs, with China having caught up to and even having overtaken the US overtime.

Context: Citations are a key measure of scientific performance in most fields, including software engineering. However, there is limited research that studies which characteristics of articles’ metadata (title, abstract, keywords, and author list) are driving citations in this field. Objective: In this study, we propose a simple theoretical model for how citations come to be with respect to article metadata, we hypothesize theoretical linkages between metadata characteristics and citations of articles, and we empirically test these hypotheses. Method: We use multiple regression analyses to examine a data set comprising the titles, abstracts, keywords, and authors of 16,131 software engineering articles published between 1990 and 2020 in 20 highly influential software engineering venues. Results: We find that number of authors, number of keywords, number of question marks and dividers in the title, number of acronyms, abstract length, abstract propositional idea density, and corresponding authors in the core Anglosphere are significantly related to citations. Conclusion: Various characteristics of articles’ metadata are linked to the frequency with which the corresponding articles are cited. These results partially confirm and partially go counter to prior findings in software engineering and other disciplines.