85,895 research outputs found
Qualitative software engineering research -- reflections and guidelines
Researchers are increasingly recognizing the importance of human aspects in
software development and since qualitative methods are used to, in-depth,
explore human behavior, we believe that studies using such techniques will
become more common.
Existing qualitative software engineering guidelines do not cover the full
breadth of qualitative methods and knowledge on using them found in the social
sciences. The aim of this study was thus to extend the software engineering
research community's current body of knowledge regarding available qualitative
methods and provide recommendations and guidelines for their use.
With the support of an epistemological argument and a literature review, 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.Comment: 30 page
Consequences of Unhappiness While Developing Software
The growing literature on affect among software developers mostly reports on
the linkage between happiness, software quality, and developer productivity.
Understanding the positive side of happiness -- positive emotions and moods --
is an attractive and important endeavor. Scholars in industrial and
organizational psychology have suggested that also studying the negative side
-- 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, and is mainly expressed in terms of
development productivity and software quality. In this paper, we attempt to
uncover the experienced consequences of unhappiness among software developers.
Using qualitative data analysis of the responses given by 181 questionnaire
participants, we identified 49 consequences of unhappiness while doing software
development. We found detrimental consequences on developers' mental
well-being, the software development process, and the produced artifacts. Our
classification scheme, available as open data, will spawn 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.Comment: 6 pages. To be presented at the Second International Workshop on
Emotion Awareness in Software Engineering, colocated with the 39th
International Conference on Software Engineering (ICSE'17). Extended version
of arXiv:1701.02952v2 [cs.SE
How Do You Feel, Developer? An Explanatory Theory of the Impact of Affects on Programming Performance
Affects---emotions and moods---have an impact on cognitive activities and the
working performance of individuals. Development tasks are undertaken through
cognitive processes, yet software engineering research lacks theory on affects
and their impact on software development activities. In this paper, we report
on an interpretive study aimed at broadening our understanding of the
psychology of programming in terms of the experience of affects while
programming, and the impact of affects on programming performance. We conducted
a qualitative interpretive study based on: face-to-face open-ended interviews,
in-field observations, and e-mail exchanges. This enabled us to construct a
novel explanatory theory of the impact of affects on development performance.
The theory is explicated using an established taxonomy framework. The proposed
theory builds upon the concepts of events, affects, attractors, focus, goals,
and performance. Theoretical and practical implications are given.Comment: 24 pages, 2 figures. Postprin
The Effects of the Quantification of Faculty Productivity: Perspectives from the Design Science Research Community
In recent years, efforts to assess faculty research productivity have focused more on the measurable quantification of academic outcomes. For benchmarking academic performance, researchers have developed different ranking and rating lists that define so-called high-quality research. While many scholars in IS consider lists such as the Senior Scholar’s basket (SSB) to provide good guidance, others who belong to less-mainstream groups in the IS discipline could perceive these lists as constraining. Thus, we analyzed the perceived impact of the SSB on information systems (IS) academics working in design science research (DSR) and, in particular, how it has affected their research behavior. We found the DSR community felt a strong normative influence from the SSB. We conducted a content analysis of the SSB and found evidence that some of its journals have come to accept DSR more. We note the emergence of papers in the SSB that outline the role of theory in DSR and describe DSR methodologies, which indicates that the DSR community has rallied to describe what to expect from a DSR manuscript to the broader IS community and to guide the DSR community on how to organize papers for publication in the SSB
Evolution of statistical analysis in empirical software engineering research: Current state and steps forward
Software engineering research is evolving and papers are increasingly based
on empirical data from a multitude of sources, using statistical tests to
determine if and to what degree empirical evidence supports their hypotheses.
To investigate the practices and trends of statistical analysis in empirical
software engineering (ESE), this paper presents a review of a large pool of
papers from top-ranked software engineering journals. First, we manually
reviewed 161 papers and in the second phase of our method, we conducted a more
extensive semi-automatic classification of papers spanning the years 2001--2015
and 5,196 papers. Results from both review steps was used to: i) identify and
analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well
as relevant trends in usage of specific statistical methods (e.g.,
nonparametric tests and effect size measures) and, ii) develop a conceptual
model for a statistical analysis workflow with suggestions on how to apply
different statistical methods as well as guidelines to avoid pitfalls. Lastly,
we confirm existing claims that current ESE practices lack a standard to report
practical significance of results. We illustrate how practical significance can
be discussed in terms of both the statistical analysis and in the
practitioner's context.Comment: journal submission, 34 pages, 8 figure
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