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.

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.

A key goal of empirical research in software engineering is to assess practical significance, which answers the question whether the observed effects of some compared treatments show a relevant difference in practice in realistic scenarios. Even though plenty of standard techniques exist to assess statistical significance, connecting it to practical significance is not straightforward or routinely done; indeed, only a few empirical studies in software engineering assess practical significance in a principled and systematic way. In this paper, we argue that Bayesian data analysis provides suitable tools to assess practical significance rigorously. We demonstrate our claims in a case study comparing different test techniques. The case study's data was previously analyzed (Afzalet al., 2015) using standard techniques focusing on statistical significance. Here, we build a multilevel model of the same data, which we fit and validate using Bayesian techniques. Our method is to apply cumulative prospect theory on top of the statistical model to quantitatively connect our statistical analysis output to a practically meaningful context. This is then the basis both for assessing and arguing for practical significance. Our study demonstrates that Bayesian analysis provides a technically rigorous yet practical framework for empirical software engineering. A substantial side effect is that any uncertainty in the underlying data will be propagated through the statistical model, and its effects on practical significance are made clear. Thus, in combination with cumulative prospect theory, Bayesian analysis supports seamlessly assessing practical significance in an empirical software engineering context, thus potentially clarifying and extending the relevance of research for practitioners.

There is some ambiguity of what agile means in both research and practice. Authors have suggested a diversity of different definitions, through which it is difficult to interpret what agile really is. The concept, however, exists in its implementation through agile practices. In this vision paper, we argue that adopting an agile approach boils down to being more responsive to change. To support this claim, we relate agile principles, practices, the agile manifesto, and our own experiences to this core definition. We envision that agile transformations would be, and are, much easier using this definition and contextualizing its implications.

Abstract The values of software organizations are crucial for achieving high performance; in particular, agile development approaches emphasize their importance. Researchers have thus far often assumed that a specific set of values, compatible with the development methodologies, must be adopted homogeneously throughout the company. It is not clear, however, to what extent such assumptions are accurate. Preliminary findings have highlighted the misalignment of values between groups as a source of problems when engineers discuss their challenges. Therefore, in this study, we examine how discrepancies in values between groups affect software companies' performance. To meet our objectives, we chose a mixed method research design. First, we collected qualitative data by interviewing fourteen ( N = 14) employees working in four different organizations and processed it using thematic analysis. We then surveyed seven organizations ( N = 184). Our analysis indicated that value misalignment between groups is related to organizational performance. The aligned companies were more effective, more satisfied, had higher trust, and fewer conflicts. Our efforts provide encouraging findings in a critical software engineering research area. They can help to explain why some companies are more efficient than others and, thus, point the way to interventions to address organizational challenges.

Employees’ attitudes towards organizational change are a critical determinant in the change process. Researchers have therefore tried to determine what underlying concepts that affect them. These extensive efforts have resulted in the identification of several antecedents. However, no studies have been conducted in a software engineering context and the research has provided little information on the relative impact and importance of the identified concepts. In this study, we have combined results from previous social science research with results from software engineering research, and thereby identified three underlying concepts with an expected significant impact on software engineers’ attitudes towards organizational change, i.e. their knowledge about the intended change outcome, their understanding of the need for change , and their feelings of participation in the change process. The result of two separate multiple regression analysis, where we used industrial questionnaire data (N=56), showed that the attitude concept openness to change is predicted by all three concepts, while the attitude concept readiness for change is predicted by need for change and participation . Our research provides an empirical baseline to an important area of software engineering and the result can be a starting-point for future organizational change research. In addition, the proposed model prescribes practical directions for software engineering organizations to adopt in improving employees’ responses to change and, thus, increase the probability of a successful change.

Throughout the history of software engineering , the human aspects have repeatedly been recognized as important. Even though research that investigates them has been growing in the past decade, these aspects should be more generally considered. The main objective of this study is to clarify the research area concerned with human aspects of software engineering and to create a common platform for future research. In order to meet the objective, we propose a definition of the research area behavioral software engineering (BSE) and present results from a systematic literature review based on the definition. The result indicates that there are knowledge gaps in the research area of behavioral software engineering and that earlier research has been focused on a few concepts, which have been applied to a limited number of software engineering areas. The individual studies have typically had a narrow perspective focusing on few concepts from a single unit of analysis. Further, the research has rarely been conducted in collaboration by researchers from both software engineering and social science. Altogether, this review can help put a broader set of human aspects higher on the agenda for future software engineering research and practice.