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Publish or Perish
Questioning the Impact of Our Research on
the Software Developer
Margaret-Anne Storey
@margaretstorey
Special Thanks
Jo Atlee • Brynn Hawker
Cassandra Petrachenko
My husband and kids
Lion Man2
Developers3
What do we make?4
Matt Warcholinski
Do we need developers?
How to Stay Relevant as a Software
Developer in the Age of AI
5
6
Developer Study
#1
41.8%
Distracting work
Environment
#2
36.6%
Meetings
#3
36.5%
Non-development
work
7
Context
Human / Social
Aspects
Technical
Aspects
Socio-Technical
Aspects
Software Engineering Design Space8
Human / Social Technical
Socio-Technical
Productivity Paradox9
Human / Social Technical
Socio-Technical
Joint Optimization – Code Review
CodeFlow
CodeFlow: Improving the Code Review
Process at Microsoft, Czerwonka et al. 2018.
10
11
Human / Social
Aspects
Technical
Aspects
Socio-Technical
Aspects
Software Engineering Design Space12
Software Engineering Research Space?
Human / Social
Aspects
Technical
Aspects
Socio-Technical
Aspects
13
Background
Questioning Our Impact
Paradigms
Contributions
Methods
Improving Our Impact
14
Research Collaborators
Per Runeson
Emelie Engström
Martin Höst
Elizabeth Bjarnason
and
Teresa Baldassarre (Bari)
Arie van Deursen (Delft)
...
Jacek Czerwonka
Brendan Murphy
Tom Zimmermann
Chris Bird
Kim Herzig
Laura MacLeod
Elena Voyloshnikova
Carly Lebeuf
Courtney Williams
Eirini Kalliamvakou
Neil Ernst
Daniel German
Alexey Zagalsky
The CHISEL Group
...
15
Background
Questioning Our Impact
Paradigms
Contributions
Methods
Improving Our Impact
16
“A paradigm is a shared world view that
represents the beliefs and values in a
discipline and that guides how problems
are solved.”
– Schwandt, 2001
17
Paradigms – Postpositivism
Scientific method
Evidence-based reality
Theory verification and falsification
Quantitative over qualitative
18
Paradigms – Constructivism
Reality is subjective and experiential
Theory generation
Biases are expected and made explicit
Qualitative over quantitative
19
Paradigms – Advocacy / Participatory
Change oriented
Collaborative
Shaped by political and social lenses
Qualitative and quantitative
20
Postpositivism Advocacy /
Participatory
Constructivism
Paradigms21
Margaret BurnettCarolyn SeamanTim Menzies
Activity
Postpositivism Constructivism
Advocacy /
Participatory
22
Go to: menti.com
Enter code: 48 84 23
“I am the publish or perish,
whatever works guy.”
Paradigms
Postpositivism Constructivism
Advocacy /
Participatory
Problem centered • Real-world practice oriented
Pragmatism
23
Background
Questioning Our Impact
Paradigms
Contributions
Methods
Improving Our Impact
24
Formal
Sciences
Philosophical
Mathematical foundations
Design
Sciences
Medical treatments
Engineering solutions
Explanatory
Sciences
Descriptive theories
Predictive theories
Types of Contributions
Empirical Research
25
Lund University26
Environment
Design Science — Hevner (2007)
Design Science
Knowledge Base
Relevance
Cycle
Rigor Cycle
Design Cycle
27
Practice
Theory
Problem
Constructs
Solution
Constructs
Problem
Instance(s)
Solution
Instance(s)
Problem
Characterization
Analytical
Validation
Instantiation or
Abstraction
Empirical
Validation
Design Science — Our View Problem Solution28
Design Science — Our View (Simplified)
Problem
Instance
Solution
Requirements
Validation
Solution
Evaluation
Problem
Understanding
Technological Rule
29
Technological Rule
(Theory Fragment)
Technological Rules
To reduce errors in open source projects
use continuous integration.
To achieve an effect in a given context use / do intervention.
𝑥 𝑦 𝑧
30
Evaluation Criteria
Problem
Instance
Solution
Requirements
Validation
Solution
Evaluation
Problem
Understanding
31
Evaluation Criteria
🅐
Criteria
🅐 Relevance
Problem
Instance
Solution
Requirements
Validation
Solution
Evaluation
Problem
Understanding
32
Evaluation Criteria
🅑
🅑🅑 Criteria
🅐 Relevance
🅑 Rigor
🅐
Problem
Instance
Solution
Requirements
Validation
Solution
Evaluation
Problem
Understanding
33
Technological Rule
Evaluation Criteria
🅒
🅑
🅑🅑 Criteria
🅐 Relevance
🅑 Rigor
🅒 Novelty
🅐
Problem
Instance
Solution
Requirements
Validation
Solution
Evaluation
Problem
Understanding
34
Design Science Visual Abstract Template
Problem Instance Solution
Requirements
Validation
Solution
Evaluation
Problem
Understanding
Technological Rule
🅐 Relevance 🅑 Rigor 🅒 Novelty
35
Review of ICSE Distinguished Papers from 2014 to 2018
38Papers
36
Applying Visual Abstracts37
How were papers clustered?
Problem Constructs Design Constructs
Problem Instance(s) Design Instance(s)
⬤ Descriptive
⬤ Problem Solution
⬤ Solution Validation
⬤ Solution Design
⬤ Meta
8
7
7
13
Meta
3
PracticeTheory
38
Results for ICSE Distinguished Papers from 2014 to 2018
Rigor
Novelty
Relevance
A+
A
F
Design Science Criteria
39
Problem Constructs Design Constructs
Problem Instance(s) Design Instance(s)⬤ Descriptive
⬤ Problem Solution
⬤ Solution Validation
⬤ Solution Design
5/8
2/7
0/7
6/13
Relevance to stakeholders?
13/35
40
Consider Stakeholders
Background
Questioning Our Impact
Paradigms
Contributions
Methods
Improving Our Impact
41
Field
DataRespondent
Lab
Meta
Formal Theory
Socio-Technical Research Framework
Non-Empirical Empirical
42
Field
Experiments
Studies
Data
In-silico
Retrospective
Respondent
Surveys
Interviews
Lab
Experiments
Studies
Realism
Generalizability
Control
(human actors)
Precision
(data measurements)
Research Methods and Tradeoffs
Realism
Generalizability
Control
(human actors)
43
Lebeuf, Voyloshnikova, Herzig & Storey: 
“Debugging, and Optimizing Distributed
Software Builds: A Design Study”, ICMSE 2018
Field
DataRespondent
Lab
#1#2
The Methods We Chose
Realism
Control
(human actors)
44
Gousios, Storey & Bacchelli,
“Work Practices and Challenges in Pull-Based
Development: The Contributor’s Perspective”, ICSE 2016
Field
DataRespondent
Lab
#1 #2
The Methods We Chose
Generalizability Precision
45
Categorizing ICSE Paper Research Methods
253Technical Track Papers
2015 to 2017
46
Field
DataRespondent
Lab
Meta
Formal Theory
19536
22 26
13
7
Categorizing ICSE Paper Research Methods47
Field
DataRespondent
Lab
19536
22
26
Categorizing Research Methods
Control
(human actors)
Precision
(data measurements)
Realism
Generalizability
48
Meta
Formal
Theory
37 Data Papers Used Triangulation
Field
DataRespondent
Lab
4
8
12
6 7
195
26
36
22
7
13
49
Data Only Papers
158
Contribution Type
Solution
Descriptive
50
22
136
110of 158
Authors Mention
Developers
Data Only Papers
(No human research subjects)
70%
And yet...
158
According to the Authors51
“Our results provide initial evidence that
several assumptions made by automated
debugging techniques do not hold in
practice.”
– Parnin & Orso, ISSTA 2011
Solution Study Implications52
“You are smarter than your data.
Data do not understand causes
and effects; humans do.”
– Pearl and Mackenzie
The Book of Why
53
Questioning Our Impact
Paradigms Methods Contributions
Postpositivism
Constructivism
Advocacy /
Participatory
Problem
Constructs
Design
Constructs
Problem
Instance(s)
Design
Instance(s)
5/8
2/7
0/7
6/13
Field
DataRespondent
Lab
19536
22 26
54
Background
Questioning Our Impact
Improving Our Impact
Paradigms
Methods
Contributions
55
Creating Silos56
INDUSTRY PROGRAM SEIP TECHNICAL TRACK
Conference Structures
300+
Papers
57
Assigning Reviewers58
ICSE Paper Reviewing Criteria
Significance Soundness Verifiability
Novel and adds to existing
knowledge
Supports independent
verification or replication
Rigor of appropriate
research methods
Current
Stakeholder involvement
Scales to industry
Triangulation of realism
Generalizability
Control of humans
Audit trails
Member checking
Biases & reactivity
Future
59
Background
Questioning Our Impact
Improving Our Impact
Paradigms
Methods
Contributions
60
Why these methods?
“We also would have conducted a
field experiment […], but we
didn’t have subjects readily
available.”
“We took the standard approach
that would typically be reported
in a [topic] conference.”
61
Actionable
Study adopters
Non adopters
Fast
Quantitative
Perceptive
Human factors
Hypotheticals
Controlled
Scalable
Repeatable
Unobtrusive
Diverse Collaboration ↔ Diverse Methods
Field
DataRespondent
Lab
62
Interviews /
Observations
Surveys Telemetry
The Power of Diverse Methods63
Data System
Background
Questioning Our Impact
Improving Our Impact
Paradigms
Methods
Contributions
64
Badges65
Technological Rule Developer Tested Industry CollaborationTriangulation
Some New Badges66
Brynn Hawker @bnhwkr
Write less, think hard, imagine more.
Margaret-Anne Storey
@margaretstorey
“Using a visual abstract as a lens for communicating and promoting design science
research in software engineering”, Storey, Engström, Höst, Runeson, Bjarnason, ESEM
2017. http://chisel.cs.uvic.ca/pubs/storey-ESEM2017.pdf
“A review of software engineering research from a design science Perspective”, Engström,
Storey, Runeson, Höst, Baldassarre, Arxiv 2019. http://arxiv.org/abs/1904.12742
“Methodology Matters: How We Study Socio-Technical Aspects in Software Engineering.”,
Courtney Williams, Margaret-Anne Storey, Neil A. Ernst, Alexey Zagalsky and Eirini
Kalliamvakou. 2019. Arxiv (forthcoming)
Special thanks to Brynn Hawker @bnhwkr for slide and graphic design!
Key references68
Zelkowitz & Wallace, “Experimental Models for Validating Technology,”
1998
Shaw, “Writing good software engineering research papers,” 2003
Vessey, Ramesh & Glass, “A unified classification system for research in
the computing disciplines,” 2005
Smite, Wohlin, Gorschek & Feldt, “Empirical evidence in global software
engineering: a systematic review,” 2010
Wohlin & Aurum, “Towards a decision-making structure for selecting a
research design in empirical software engineering,” 2015
Stol, Ralph & Fitzgerald, “Grounded theory in software engineering
research: A critical review and guidelines,” 2016
Runeson & Höst, “Guidelines for conducting and reporting case study
research in software engineering,” 2008
Feldt & Magazinius, "Validity Threats in Empirical Software Engineering
Research-An Initial Survey," 2010
Siegmund, Siegmund & Apel, “Views on internal and external validity in
empirical software engineering,” 2015
Bibliography
Wohlin et al., ”Experimentation in software engineering,” 2012
Sjøberg et al., "Building theories in software engineering," 2008
Stol & Fitzgerald, "Uncovering theories in software engineering," 2013
Ralph, "Possible core theories for software engineering," 2013
Shneiderman, "Twin-Win Model: A human-centered approach to
research success," 2018
Easterbrook, Singer, Storey & Damian, “Selecting empirical methods for
software engineering research,” 2008
Shaw, "What makes good research in software engineering," 2002
Creswell, "A Concise Introduction to Mixed Methods Research, " 2014
Hevner, "A three cycle view of design science research," 2007
Van Aken, "Management Research Based on the Paradigm of the Design
Sciences: The Quest for Field‐Tested and Grounded Technological
Rules,” 2004
69

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