To consolidate a body of knowledge built upon evidence, experimental results have to be extensively verified. Experiments need replication at other times and under other conditions before they can produce an established piece of knowledge. Several replications need to be run to strengthen the evidence. Most SE experiments have not been replicated. If an experiment is not replicated, there is no way to distinguish whether results were produced by chance (the observed event occurred accidentally), results are artifactual (the event occurred because of the experimental configuration but does not exist in reality) or results conform to a pattern existing in reality. The immaturity of experimental SE knowledge has been an obstacle to replication. Context differences usually oblige SE experimenters to adapt experiments for replication. As key experimental conditions are yet unknown, slight changes in replications have led to differences in the results that prevent verification. There are still many uncertainties about how to proceed with replications of SE experiments. Should replicators reuse the baseline experiment materials? How much liaison should there be among the original and replicating experimenters, if any? What elements of the experimental configuration can be changed for the experiment to be considered a replication rather than a new experiment? The aim of replication is to verify results, but different types of replication serve special verification purposes and afford different degrees of change. Each replication type helps to discover particular experimental conditions that might influence the results. We need to learn which types of replications are feasible in SE as well as the acceptable changes for each type and the level of verification provided.

1. Towards Understanding
the Replication of
SE Experiments
Natalia Juristo
Universidad Politecnica de Madrid (Spain)
&
University of Oulu (Finland)
ESEM Conference Baltimore (USA) October 11th, 2013

2. Scope & Terminology
 This talk focuses on the replication of
experiments
 I will refer to the study whose results we
want to check as the baseline experiment

3. Replication Intuitive Definition
 Deliberate repetition of research
procedures in a second investigation for
the purpose of determining if earlier
results can be reproduced

4. Content
 Replication & the experimental paradigm
 State of replication in ESE: practice & theory
 Shedding a bit of light
 Purposes for replicating
 Replication functions
 Some answers
 Replication limits
 Baseline and replication minimum degree of similarity
 Admissible changes
 Reproduction of results
 Threats to reuse materials
 Summary

5. Role of Replication in
Experimental Paradigm

6. Searching for Regularities
 Science does not settle for anecdotes. A
scientific law or theory describes a regular
occurrence in the world
 Regularities existing in reality are identified by
reproducing the same event in different
replications
 The result of one experiment is an isolated
event

7. One Result, Three Meanings
 Without reproduction of results it is impossible
to distinguish whether they
 occurred by chance
 are artifactual
 the event occurs only in the experiment, not in reality
 really correspond to a regularity

8. State of Replication in ESE
Practice

9. Not Enough Replications
 Most SE experiments have not yet been
replicated
 Two reviews provide empirical data to
support this point
 Let us look at their results

10. Experiments in Leading Journals
& Conferences (1993-2002)
 5,453 articles published from 1993 to 2002
in major SE journals and conference
proceedings
 113 experiments
 20 (17.7%) described as replications
Sjøberg et al. “A survey of
controlled experiments in SE” TSE, 2005

11. All Publications
 96 papers reporting replications
 133 replications of 72 baseline studies
 Any type of empirical study
 Quasi-Experiments 35 49%
 Controlled Experiments 21 29%
 Case Study 15 21%
 Survey 1 1%
da Silva et al. “Replication of Empirical Studies in
Software Engineering Research: A Systematic
Mapping Study” EMSE 2013

12. Mostly Internal & Not Stand-Alone
Publications [Da Silva et al. 2013]
Internal – Original-Included reports
Internal – Replication-only reports
1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
0
0 0 2 1 1 1 3 1 3 6 1 3 5 2 4 1
3 0 0 3 1 2 2 1 3 11 7 5 2 6 14
1996
0
0
NumberofReplications
2
4
6
8
10
12
14
16
Total 1 5 6 4 5 6 6 4 4 9 15 11 13 9 13 220
18
20
22
External 1 2 4 3 1 4 1 1 0 0 3 1 3 5 3 70

13. First Paper Published on a
Replication [Da Silva et al. 2012]
 In SE, the first article that explicitly reported
a replication of an empirical study was
published in 1994
 Daly, Brooks, Miller, Roper & Wood
Verification of Results in Sw Maintenance Through
External Replication
Intl Conf on Software Maintenance

14. EMSE Special Issue on
Replication
 The large number of submissions was
admittedly more than we expected
 We received a total of 16 submissions
 Encouraging the publication of replications
will foster researchers to replicate more
studies

15. State of Replication in ESE
Theory

16. First Theoretical Publication on
Replication
 In 1999, a paper discussed a framework to
organize sets of related experiments
(families) and the generation of knowledge
from such sets
 Basili, Shull & Lanubile. Building knowledge through
families of experiments. TSE
 Is a family exactly a set of replications?
 …experiments can be viewed as part of common
families of studies, rather than being isolated
events…

17. More Activity in the Last 10 Years
 Shull, Basili, Carver, Maldonado, Travassos, Mendonça & Fabbri Replicating
software engineering experiments: Addressing the tacit knowledge problem.
ISESE 2002
 Vegas, Juristo, Moreno, Solari & Letelier Analysis of the influence of
communication between researchers on experiment replication. ISESE 2006
 Brooks, Roper, Wood, Daly & Miller Replication’s role in software engineering.
Guide to Advanced Empirical SE. Springer 2008
 Juristo & Vegas Using differences among replications of software engineering
experiments to gain knowledge. ESEM 2009 [Juristo & Vegas The Role of Non-
Exact Replications in SE EMSE Journal 2011]
 Krein & Knutson A Case for replication: Synthesizing research methodologies in
SE. RESER 2010
 Gómez, Juristo & Vegas Replications types in experimental disciplines. ESEM
2010
 Juristo, Vegas, Solari, Abrahao & Ramos A Process for Managing Interaction
between Experimenters to Get Useful Similar Replications IST 2013

18. State of the Theory
 There is no agreement yet on terminology,
typology, purposes, operation and other
replication issues
 There is not even agreement on what a
replication is!!
 Different authors consider different types of
changes to the baseline experiment as
admissible

19. Example of Divergent Views
 Some researchers advise the use of different protocols
and materials to preserve independence and prevent
error propagation in replications by using the same
configuration
 Kitchenham
The role of replications in ESE - a word of warning EMSE 2008
 Other researchers recommend the reuse of materials to
assure that replications are similar enough for results to
be comparable
 Shull, Carver, Vegas & Juristo
The role of replications in ESE EMSE 2008

20. Shedding some Light on
Replication
Role of Replication

21. The Two Roles of Replication
 Validation
 Learning

22. Learning Relevant Conditions
 As more replications of Thompson and
McConnell’s baseline experiment were run
 different conditions influencing the results of this
experiment were identified
 After several hundred experiments had been run
 experimenters managed to identify around 70
conditions influencing the behavior of this type of
invertebrate

23. Which are the Important
Variables?
 “…In fact, the principle of Transversely
Excited Atmospheric (TEA) lasers,
scientists did not know that the inductance
of the top was important”
A physicist quoted in
Changing Order: Replication and Induction in Scientific Practice
Harry Collins 1992

24. First Learn, Then Validate
 “In the early stages, failure to get the expected
results is not falsification but a step in the
discovery of some interfering factor.
For immature experimental knowledge, the first
step is … to find out which experimental
conditions should be controlled”
Validity and the Research Process
Brinberg and McGrath 1985

25. SE Problems with Identical
Replications
 SE has tried to repeat experiments identically,
but no exact replications have yet been
achieved
 The complexity of the software development
setting prevents the many experimental
conditions from being reproduced identically
 Yet this is a regular rather than an exceptional
situation

26. In the Beginning Most is Unkown
 “Most aspects are unknown when we start
to study a phenomenon experimentally.
Even the tiniest change in a replication
can lead to inexplicable differences in the
results”
Validity and the Research Process
Brinberg and McGrath 1985

27. Start with Similar Replications
 “The less that is known about an area the more
power a very similar experiment has ... This is
because, in the absence of a well worked out set
of crucial variables, any change in the
experiment configuration, however trivial in
appearance, may well entail invisible but
significant changes in conditions”
Changing Order:
Replication and Induction in Scientific Practice
Harry Collins 1992

28. Learning & Validation Process
1. Start with identical replications
 At the beginning of experimental research, equality, even if
targeted, will not happen
 There will be either invisible but significant changes in conditions or
induced changes due to context adaptation or both
 Failure to get the expected results should not be construed as
falsification, but as a step towards the discovery of some new
factor
2. Later on, both knowledge discovery and testing
can be more systematic
 Changes in the configuration will be made purposely to learn
more variables and rule out artifactual results

29. Learning is Even More Important
 Replication is needed not merely to
validate one’s findings, but more
importantly, to establish the increasing
range of radically different conditions
under which the findings hold, and the
predictable exceptions
The design of replicated studies. American Statistician
Lindsay and Ehrenberg 1993

30. Shedding some Light on
Replication
Functions of Replication

31. Reminder of Experimental Setting
 Operationalization
 Treatments
 Response variable
 Protocol
 Experimental design
 Experimental objects
 Guides
 Measuring instruments
 Data analysis techniques
 Population
 Objects
 Subjects

32. Verification Functions
 Control experimental errors
 Control protocol independence
 Understand operationalization limits
 Understand population limits

33. Control Experimental Errors
 Verify that the results of the baseline experiment
are not a chance product of an error
 All elements of the experiment must resemble
the baseline experiment as closely as possible
 Collateral benefit
 Provide an understanding of the natural (random)
variation of the observed results
 critical for being able to decide whether or not results hold in
dissimilar replications

34. Control Protocol Independence
 Verify that the results of the baseline experiment
are not artifactual
 An artifactual result is due to the experimental
configuration and cannot be guaranteed to exist in
reality
 The experimental protocol needs to be changed
for this purpose
 If an experiment is replicated several times using the
same materials, the observed results may occur due
to the materials
 The same applies for all protocol elements

35. Understanding Operationalization
Limits
 Learn how sensitive results are to different
operationalizations
 Treatment operationalizations
 treatment application procedures, treatment
instructions, resources, treatment
transmission …
 Effect operationalizations
 Metrics, measurement procedures …

36. Understand Population Limits
 Learn the extent to which results hold for
other subject types or other types of
experimental objects
 Learn to which specific population the
experimental sample belongs and what
the characteristics of such population are

37. Changes & Replication Functions
Experimental
Configuration
Control
Experimental
Error
Control Protocol
Independence
Understand
Operationalization
Limits
Understand
Population Limits
Operationalization = = ≠ =
Population = = = ≠
Protocol = ≠ = =
Function of Replication
LEGEND: = the element is equal to, or as similar as possible to, the baseline experiment
≠ the element varies with respect to the baseline experiment

38. Some Answers

39. Question 1
What exactly is a replication
study?

40. Establishing Limits
Amount of Changes
Run
Same data
different
models or
statistical
methods
Identical
same site &
researchers
+
Protocol
changes
Operationa-
lization
changes
Population
changes
Just the
hypothesis
kept
RE-ANALYSIS REPETITION REPLICATION REPRODUCTION
Not Run

41. Question 2
What level of similarity should an
experiment have to be
considered a replication rather
than a new experiment?

42. Unchanged elements of a
replication
 A replication must share the hypothesis
with the baseline experiment
 Same response variable
 although not same metric
 Same treatments
 although not same operationalization
 at least two treatments in common

43. Partials Replications
Exp. A (Baseline OR Replication)
Exp. B (Replication OR Baseline)
Exp. D
Replication C
RV3
RV2
RV1
T5
T4
T3
T2
T6
T1

44. Question 3
What changes are acceptable?

45. Levels of Verification
 Similarity between the baseline
experiment and a replication serve
different verification purposes depending
on the changes made
 Replicating an experiment as closely as possible
 Verifies results are not accidental
 Varying the experimental protocol
 Verifies results are not artifactual
 Varying the population properties
 Verifies types of populations for which the results hold
 Varying the operationalization
 Verifies range of operationalizations for which the results hold

46. Can I Change Everything?
 It is better not to change everything at the
same time
 We can understand the source of differences
in results better if only one change is made at
a time
 But a replication with a lot of changes is
not rendered useless or doomed to failure
 We will just need to wait until other
replications are run

47. Everything Different
Changes
Different
Identical
Experiment
Elements

48. One Change at a Time
Changes
Different
Iqual
Experiment
Elements

49. Increasing Changes
Changes
Different
Iqual
Experiment
Elements

50. Question 4
What is the level of similarity that
results must have to be
considered as reproduced?

51. Understanding the Natural
Variation
 Identical replications are useful for
understanding the range of variability of
results
 This provides an estimate for other
experimenters to use as a baseline when
they replicate the experiment

52. Question 5
Should replications reuse
materials?

53. Accomodating Opposing Views
 The possible threat of errors being propagated
by experimenters exchanging materials does not
mean discarding replications sharing materials
 Replications with identical materials and protocols
(and possibly the same errors) are a necessary step
for verifying that an exact replication run by others
reproduces the same results
 Other replications that alter the design and other
protocol details should be performed in order to
assure that the results are not induced by the protocol

54. Accomodating Opposing Views
 Replication functions accommodate
opposing views within a broader
framework
 Contrary stances are really tantamount to
different types of replication conducted for
different purposes
 Different ways of running replications are
useful for gradually advancing towards
verified experimental results

55. Summarizing

56. Main Ideas
 Replication in ESE
 Replication plays an essential role in the experimental paradigm
 Replication is not a regular practice in ESE today
 More methodological research on the adoption and tailoring of
replication in ESE is still necessary
 Clarifying conceptions
 Replication is necessary not merely to validate findings, but,
more importantly, to discover the range of conditions under
which the findings hold
 Replications provide different knowledge depending on the
changes to the baseline experiment
 Knowledge gained from a replication needs to relate changes
and findings

57. Towards Understanding
the Replication of
SE Experiments
Natalia Juristo
Universidad Politecnica de Madrid (Spain)
&
University of Oulu (Finland)
ESEM Conference Baltimore (USA) October 11th, 2013