We analyze developer activity across 10 major Ethereum repositories (totaling 129884 commits, 40550 issues) spanning 10 years to examine how events such as technical upgrades, market events, and community decisions impact development. Through statistical, survival, and network analyses, we find that technical events prompt increased activity before the event, followed by reduced commit rates afterwards, whereas market events lead to more reactive development. Core infrastructure repositories like Go-Ethereum exhibit faster issue resolution compared to developer tools, and technical events enhance core team collaboration. Our findings show how different types of events shape development dynamics, offering insights for project managers and developers in maintaining development momentum through major transitions. This work contributes to understanding the resilience of development communities and their adaptation to ecosystem changes.

Abstract Researchers are increasingly recognizing the importance of human aspects in software development. Because qualitative methods are used to explore human behavior in‐depth, we believe that studies using such methods will become more common. Existing qualitative software engineering guidelines do not cover the full breadth of qualitative methods and the knowledge on how to use them like in social sciences. The purpose of this study was to extend the software engineering community's current body of knowledge regarding available qualitative methods and their quality assurance frameworks and to provide recommendations and guidelines for their use. With the support of an epistemological argument and a survey of the literature, we suggest that future research would benefit from (1) utilizing a broader set of research methods, (2) more strongly emphasizing reflexivity, and (3) employing qualitative guidelines and quality criteria. We present an overview of three qualitative methods commonly used in social sciences but rarely seen in software engineering research, namely interpretative phenomenological analysis, narrative analysis, and discourse analysis. Furthermore, we discuss the meaning of reflexivity in relation to the software engineering context and suggest means of fostering it. Our paper will help software engineering researchers better select and then guide the application of a broader set of qualitative research methods.

Qualitative surveys are emerging as a popular research method in software engineering (SE), particularly as many aspects of the field are increasingly socio-technical and thus concerned with the subtle, social, and often ambiguous issues that are not amenable to a simple quantitative survey. While many argue that qualitative surveys play a vital role amongst the diverse range of methods employed in SE there are a number of shortcomings that inhibits its use and value. First there is a lack of clarity as to what defines a qualitative survey and what features differentiate it from other methods. There is an absence of a clear set of principles and guidelines for its execution, and what does exist is very inconsistent and sometimes contradictory. These issues undermine the perceived reliability and rigour of this method. Researchers are unsure about how to ensure reliability and rigour when designing qualitative surveys and reviewers are unsure how these should be evaluated. In this paper, we present a systematic mapping study to identify how qualitative surveys have been employed in SE research to date. This paper proposes a set of principles, based on a multidisciplinary review of qualitative surveys and capturing some of the commonalities of the diffuse approaches found. These principles can be used by researchers when choosing whether to do a qualitative survey or not. They can then be used to design their study. The principles can also be used by editors and reviewers to judge the quality and rigour of qualitative surveys. It is hoped that this will result in more widespread use of the method and also more effective and evidence-based reviews of studies that use these methods in the future.

Searching for clues, gathering evidence, and reviewing case files are all techniques used by criminal investigators to draw sound conclusions and avoid wrongful convictions. Medicine, too, has a long tradition of evidence-based practice, in which administering a treatment without evidence of its efficacy is considered malpractice. Similarly, in software engineering (SE) research, we can develop sound methodologies and mitigate threats to validity by basing study design decisions on evidence. Echoing a recent call for the empirical evaluation of design decisions in program comprehension experiments, we conducted a 2-phases study consisting of systematic literature searches, snowballing, and thematic synthesis. We found out (1) which validity threat categories are most often discussed in primary studies of code comprehension, and we collected evidence to build (2) the evidence profiles for the three most commonly reported threats to validity. We discovered that few mentions of validity threats in primary studies (31 of 409) included a reference to supporting evidence. For the three most commonly mentioned threats, namely the influence of programming experience, program length, and the selected comprehension measures, almost all cited studies (17 of 18) did not meet our criteria for evidence. We show that for many threats to validity that are currently assumed to be influential across all studies, their actual impact may depend on the design and context of each specific study. Researchers should discuss threats to validity within the context of their particular study and support their discussions with evidence. The present paper can be one resource for evidence, and we call for more meta-studies of this type to be conducted, which will then inform design decisions in primary studies. Further, although we have applied our methodology in the context of program comprehension, our approach can also be used in other SE research areas to enable evidence-based experiment design decisions and meaningful discussions of threats to validity.

Communication is essential in software development, and even more in distributed settings. Communication activities need to be organized and coordinated to defend against the threat of productivity losses, increases in cognitive load, and stress among team members. With a plethora of communication channels that were identified by previous research in open-source projects, there is a need to explore organizational issues in how these communication channels are introduced, explained, and motivated for use among all project members. In this study, we wanted to understand which communication channels are used in GitHub projects and how they are presented to the GitHub project audience. We employed thematic analysis to analyze 151 artifacts in 90 GitHub projects. Our results revealed 32 unique communications channels that can be divided into nine different types. Projects mostly provide channels of different types, but for some types (e.g., chat) it is common to provide several channels. Maintainers are aware that channels have different properties and help the developers to decide which channel should be used in which case. However, this is not true for all projects, and often we have not found any explicit reasons why maintainers chose to provide one channel over another. Different channels can be used for different purposes and have different affordances, so maintainers have to decide wisely which channels they want to provide and make clear which channel should be used in which case. Otherwise, developers might feel overwhelmed of too many channels and information can get fragmented over multiple channels [31].

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.

In code comprehension experiments, participants are usually told at the beginning what kind of code comprehension task to expect. Describing experiment scenarios and experimental tasks will influence participants in ways that are sometimes hard to predict and control. In particular, describing or even mentioning the difficulty of a code comprehension task might anchor participants and their perception of the task itself.

A meaningful and deep understanding of the human aspects of software engineering (SE) requires psychological constructs to be considered. Psychology theory can facilitate the systematic and sound development as well as the adoption of instruments (e.g., psychological tests, questionnaires) to assess these constructs. In particular, to ensure high quality, the psychometric properties of instruments need evaluation. In this article, we provide an introduction to psychometric theory for the evaluation of measurement instruments for SE researchers. We present guidelines that enable using existing instruments and developing new ones adequately. We conducted a comprehensive review of the psychology literature framed by the Standards for Educational and Psychological Testing. We detail activities used when operationalizing new psychological constructs, such as item pooling, item review, pilot testing, item analysis, factor analysis, statistical property of items, reliability, validity, and fairness in testing and test bias. We provide an openly available example of a psychometric evaluation based on our guideline. We hope to encourage a culture change in SE research towards the adoption of established methods from psychology. To improve the quality of behavioral research in SE, studies focusing on introducing, validating, and then using psychometric instruments need to be more common.

Static code analysis tools and integrated development environments present developers with quality-related software metrics, some of which describe the understandability of source code. Software metrics influence overarching strategic decisions that impact the future of companies and the prioritization of everyday software development tasks. Several software metrics, however, lack in validation: we just choose to trust that they reflect what they are supposed to measure. Some of them were even shown to not measure the quality aspects they intend to measure. Yet, they influence us through biases in our cognitive-driven actions. In particular, they might anchor us in our decisions. Whether the anchoring effect exists with software metrics has not been studied yet. We conducted a randomized and double-blind experiment to investigate the extent to which a displayed metric value for source code comprehensibility anchors developers in their subjective rating of source code comprehensibility, whether performance is affected by the anchoring effect when working on comprehension tasks, and which individual characteristics might play a role in the anchoring effect. We found that the displayed value of a comprehensibility metric has a significant and large anchoring effect on a developer's code comprehensibility rating. The effect does not seem to affect the time or correctness when working on comprehension questions related to the code snippets under study. Since the anchoring effect is one of the most robust cognitive biases, and we have limited understanding of the consequences of the demonstrated manipulation of developers by non-validated metrics, we call for an increased awareness of the responsibility in code quality reporting and for corresponding tools to be based on scientific evidence.

A number of qualitative studies find that team leadership is one essential success factor for evolving into a mature agile team. One such qualitative study suggests the 9-Factor Theory of Scrum Master roles, which claims that the Scrum Master performs a set of 9 leadership roles which are transferred to the team over time [ 14 ]. We aimed at conducting a quantitative exploration that examines the presence and change of the 9-Factor Theory in relation to team maturity. We conducted an online survey with 67 individuals at the conglomerate Robert Bosch GmbH. Descriptive statistics reveal that the Scrum Master and the agile team score differently on the 9 factors and that the Scrum Master role is most often distributed in teams that had been working between 3 and 5 months in an agile manner. Yet, we also find that the leadership roles predominantly remain with one dedicated Scrum Master. Based on our results we suggest to group the 9-Factor Theory into three clusters: the Scrum Master is rather linked to psychological team factors (1), while the team tends to be linked to rather product-related factors (2). Organizational factors (3) are less often present. Our practical implications suggest an extension of the Scrum Master description. Furthermore, our study lays groundwork for future quantitative testing of leadership in agile teams.

Advances in artificial intelligence have renewed interest in conversational agents. So-called chatbots have reached maturity for industrial applications. German insurance companies are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance companies by determining which classes of agents are of interest to insurance companies, finding relevant use cases and requirements, and developing a prototype for an exemplary insurance scenario. Based on this approach, we derive key findings for conversational agent implementation in insurance companies.

The growing literature on affect among software developers mostly reports on the linkage between happiness, software quality, and developer productivity. Understanding happiness and unhappiness in all its components – positive and negative emotions and moods – is an attractive and important endeavor. Scholars in industrial and organizational psychology have suggested that understanding happiness and unhappiness could lead to cost-effective ways of enhancing working conditions, job performance, and to limiting the occurrence of psychological disorders. Our comprehension of the consequences of (un)happiness among developers is still too shallow, being mainly expressed in terms of development productivity and software quality. In this paper, we study what happens when developers are happy and unhappy while developing software. Qualitative data analysis of responses given by 317 questionnaire participants identified 42 consequences of unhappiness and 32 of happiness. We found consequences of happiness and unhappiness that are beneficial and detrimental for developers’ mental well-being, the software development process, and the produced artifacts. Our classification scheme, available as open data enables new happiness research opportunities of cause-effect type, and it can act as a guideline for practitioners for identifying damaging effects of unhappiness and for fostering happiness on the job.

The happy-productive worker thesis states that happy workers are more productive. Recent research in software engineering supports the thesis, and the ideal of flourishing happiness among software developers is often expressed among industry practitioners. However, the literature suggests that a cost-effective way to foster happiness and productivity among workers could be to limit unhappiness. Psychological disorders such as job burnout and anxiety could also be reduced by limiting the negative experiences of software developers. Simultaneously, a baseline assessment of (un)happiness and knowledge about how developers experience it are missing. In this paper, we broaden the understanding of unhappiness among software developers in terms of (1) the software developer population distribution of (un)happiness, and (2) the causes of unhappiness while developing software. We conducted a large-scale quantitative and qualitative survey, incorporating a psychometrically validated instrument for measuring (un)happiness, with 2 220 developers, yielding a rich and balanced sample of 1318 complete responses. Our results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains. We also identified 219 factors representing causes of unhappiness while developing software. Our results, which are available as open data, can act as guidelines for practitioners in management positions and developers in general for fostering happiness on the job. We suggest considering happiness in future studies of both human and technical aspects in software engineering.

Similar to other industries, the software engineering domain is plagued by psychological diseases such as burnout, which lead developers to lose interest, exhibit lower activity and/or feel powerless. Prevention is essential for such diseases, which in turn requires early identification of symptoms. The emotional dimensions of Valence, Arousal and Dominance (VAD) are able to derive a person's interest (attraction), level of activation and perceived level of control for a particular situation from textual communication, such as emails. As an initial step towards identifying symptoms of productivity loss in software engineering, this paper explores the VAD metrics and their properties on 700,000 Jira issue reports containing over 2,000,000 comments, since issue reports keep track of a developer's progress on addressing bugs or new features. Using a general-purpose lexicon of 14,000 English words with known VAD scores, our results show that issue reports of different type (e.g., Feature Request vs. Bug) have a fair variation of Valence, while increase in issue priority (e.g., from Minor to Critical) typically increases Arousal. Furthermore, we show that as an issue's resolution time increases, so does the arousal of the individual the issue is assigned to. Finally, the resolution of an issue increases valence, especially for the issue Reporter and for quickly addressed issues. The existence of such relations between VAD and issue report activities shows promise that text mining in the future could offer an alternative way for work health assessment surveys.

Abstract Software engineering (SE) research lacks theory and methodologies for addressing human aspects in software development. Development tasks are undertaken through cognitive processing activities. Affects (emotions, moods, and feelings) have a linkage to cognitive processing activities and the productivity of individuals. SE research needs to incorporate affect measurements to valorize human factors and to enhance management styles. The objective of this study is to analyze the affects dimensions of valence, arousal, and dominance of software developers and their real‐time correlation with their self‐assessed productivity (sPR). A repeated measurements design was conducted with eight participants (four students and four professionals), conveniently sampled and studied individually over 90 min of programming. The analysis was performed by fitting a linear mixed‐effects (LME) model. The results showed that valence and dominance are positively correlated with the sPR. The model was able to express about 38% of deviance from the sPR. Many lessons were learned when employing psychological measurements in SE and for fitting LME. This article demonstrates the value of applying psychological tests in SE and echoes a call to valorize the human individualized aspects of software developers. It reports a body of knowledge about affects, their classification, their measurement, and the best practices to perform psychological measurements in SE with LME models. Copyright © 2014 John Wiley & Sons, Ltd.