Writing Good Software Engineering Research Papers
Proceedings of the 25th International Conference on Software Engineering, IEEE Computer Society, 2003, pp. 726-736.
Writing Good Software Engineering Research Papers
Minitutorial
Mary Shaw
Carnegie Mellon University
mary.shaw@cs.cmu.edu
Abstract
• What concrete evidence shows that your result
Software engineering researchers solve problems of
satisfies your claim?
several different kinds. To do so, they produce several
If you answer these questions clearly, you’ll probably
different kinds of results, and they should develop
communicate your result well. If in addition your result
appropriate evidence to validate these results. They often
represents an interesting, sound, and significant contribu-
report their research in conference papers. I analyzed the
tion to our knowledge of software engineering, you’ll
abstracts of research papers submitted to ICSE 2002 in
have a good chance of getting it accepted for publication
order to identify the types of research reported in the
in a conference or journal.
submitted and accepted papers, and I observed the
Other fields of science and engineering have well-
program committee discussions about which papers to
established research paradigms. For example, the
accept. This report presents the research paradigms of
experimental model of physics and the double-blind
the papers, common concerns of the program committee,
studies of medicines are understood, at least in broad
and statistics on success rates. This information should
outline, not only by the research community but also by
help researchers design better research projects and write
the public at large. In addition to providing guidance for
papers that present their results to best advantage.
the design of research in a discipline, these paradigms
establish the scope of scientific disciplines through a
Keywords: research design, research paradigms, social and political process of "boundary setting" [5].
validation, software profession, technical writing
Software engineering, however, has not yet developed
this sort of well-understood guidance. I previously [19,
1. Introduction
20] discussed early steps toward such understanding,
including a model of the way software engineering
In software engineering, research papers are customary
techniques mature [17, 18] and critiques of the lack of
vehicles for reporting results to the research community.
rigor in experimental software engineering [1, 22, 23, 24,
In a research paper, the author explains to an interested
25]. Those discussions critique software engineering
reader what he or she accomplished, and how the author
research reports against the standards of classical
accomplished it, and why the reader should care. A good
paradigms. The discussion here differs from those in that
research paper should answer a number of questions:
this discussion reports on the types of papers that are
♦ What, precisely, was your contribution?
accepted in practices as good research reports. Another
• What question did you answer?
current activity, the Impact Project [7] seeks to trace the
• Why should the reader care?
influence of software engineering research on practice.
• What larger question does this address?
The discussion here focuses on the paradigms rather than
♦ What is your new result?
the content of the research
• What new knowledge have you contributed that
This report examines how software engineers answer
the reader can use elsewhere?
the questions above, with emphasis on the design of the
•
research project and the organization of the report. Other
What previous work (yours or someone else’s)
sources (e.g., [4]) deal with specific issues of technical
do you build on? What do you provide a superior
writing. Very concretely, the examples here come from
alternative to?
the papers submitted to ICSE 2002 and the program
• How is your result different from and better than
committee review of those papers. These examples report
this prior work?
research results in software engineering. Conferences
• What, precisely and in detail, is your new result?
often include other kinds of papers, including experience
♦ Why should the reader believe your result?
reports, materials on software engineering education, and
• What standard should be used to evaluate your
opinion essays.
claim?
2. What, precisely, was your contribution?
includes all the analytic activities associated with predict-
ing, determining, and estimating properties of the software
Before reporting what you did, explain what problem
systems, including both functionality and extra-functional
you set out to solve or what question you set out to answer
properties such as performance or reliability.
—and why this is important.
Software engineering research answers questions about
2.1 What kinds of questions do software
methods of development or analysis, about details of
engineers investigate?
designing or evaluating a particular instance, about gener-
alizations over whole classes of systems or techniques, or
Generally speaking, software engineering researchers
about exploratory issues concerning existence or feasibil-
seek better ways to develop and evaluate software. Devel-
ity. Table 1 lists the types of research questions that are
opment includes all the synthetic activities that involve
asked by software engineering research papers and
creating and modifying the software, including the code,
provides specific question templates.
design documents, documentation, etc. Evaluation
Table 1. Types of software engineering research questions
Type of question
Examples
Method or means of
How can we do/create/modify/evolve (or automate doing) X?
development
What is a better way to do/create/modify/evolve X?
Method for analysis
How can I evaluate the quality/correctness of X?
or evaluation
How do I choose between X and Y?
Design, evaluation, or How good is Y? What is property X of artifact/method Y?
analysis of a
What is a (better) design, implementation, maintenance, or adaptation for application X?
particular instance
How does X compare to Y?
What is the current state of X / practice of Y?
Generalization or
Given X, what will Y (necessarily) be?
characterization
What, exactly, do we mean by X? What are its important characteristics?
What is a good formal/empirical model for X?
What are the varieties of X, how are they related?
Feasibility study or
Does X even exist, and if so what is it like?
exploration
Is it possible to accomplish X at all?
The first two types of research produce methods of
technique). One reasonable interpretation is that the
development or of analysis that the authors investigated in
traditional engineering disciplines are much more mature
one setting, but that can presumably be applied in other
than HCI, and so the character of the research might
settings. The third type of research deals explicitly with
reasonably differ [17, 18]. Also, it appears that different
some particular system, practice, design or other instance
disciplines have different expectations about the "size" of
of a system or method; these may range from narratives
a research result—the extent to which it builds on existing
about industrial practice to analytic comparisons of
knowledge or opens new questions. In the case of ICSE,
alternative designs. For this type of research the instance
the kinds of questions that are of interest and the minimum
itself should have some broad appeal—an evaluation of
interesting increment may differ from one area to another.
Java is more likely to be accepted than a simple evaluation
of the toy language you developed last summer.
2.2 Which of these are most common?
Generalizations or characterizations explicitly rise above
The most common kind of ICSE paper reports an
the examples presented in the paper. Finally, papers that
improved method or means of developing software—that
deal with an issue in a completely new way are sometimes
is, of designing, implementing, evolving, maintaining, or
treated differently from papers that improve on prior art,
otherwise operating on the software system itself. Papers
so "feasibility" is a separate category (though no such
addressing these questions dominate both the submitted
papers were submitted to ICSE 2002).
and the accepted papers. Also fairly common are papers
Newman's critical comparison of HCI and traditional
about methods for reasoning about software systems,
engineering papers [12] found that the engineering papers
principally analysis of correctness (testing and
were mostly incremental (improved model, improved
verification). Analysis papers have a modest acceptance
technique), whereas many of the HCI papers broke new
edge in this very selective conference.
ground (observations preliminary to a model, brand new
Table 2 gives the distribution of submissions to ICSE
the table gives the number of papers submitted and ac-
2002, based on reading the abstracts (not the full papers—
cepted, the percentage of the total paper set of each kind,
but remember that the abstract tells a reader what to ex-
and the acceptance ratio within each type of question.
pect from the paper). For each type of research question,
Figures 1 and 2 show these counts and distributions.
Table 2. Types of research questions represented in ICSE 2002 submissions and acceptances
Type of question
Submitted
Accepted
Ratio Acc/Sub
Method or means of
development
142(48%)
18
(42%)
(13%)
Method for analysis or evaluation
95 (32%)
19 (44%)
(20%)
Design, evaluation, or analysis of a particular instance
43 (14%)
5 (12%)
(12%)
Generalization or characterization
18 (6%)
1 (2%)
(6%)
Feasibility study or exploration
0 (0%)
0 (0 %)
(0%)
TOTAL
298(100.0%)
43 (100.0%)
(14%)
Question
Question
300
100%
250
80%
200
60%
150
40%
100
50
20%
0
0%
Devel Analy
Eval
Gener
Feas
Total
Devel Analy
Eval
Gener Feas
Total
Accepted
Rejected
Accepted
Rejected
Figure 1. Counts of acceptances and rejections
Figure 2. Distribution of acceptances and rejections
by type of research question
by type of research question
2.3 What do program committees look for?
3.1 What kinds of results do software engineers
Acting on behalf of prospective readers, the program
produce?
committee looks for a clear statement of the specific
The tangible contributions of software engineering
problem you solved—the question about software devel-
research may be procedures or techniques for develop-
opment you answered—and an explanation of how the
ment or analysis; they may be models that generalize from
answer will help solve an important software engineering
specific examples, or they may be specific tools, solutions,
problem. You'll devote most of your paper to describing
or results about particular systems. Table 3 lists the types
your result, but you should begin by explaining what
of research results that are reported in software engineer-
question you're answering and why the answer matters.
ing research papers and provides specific examples.
If the program committee has trouble figuring out
whether you developed a new evaluation technique and
3.2 Which of these are most common?
demonstrated it on an example, or applied a technique you
By far the most common kind of ICSE paper reports a
reported last year to a new real-world example, or
new procedure or technique for development or analysis.
evaluated the use of a well-established evaluation
Models of various degrees of precision and formality were
technique, you have not been clear.
also common, with better success rates for quantitative
than for qualitative models. Tools and notations were well
3. What is your new result?
represented, usually as auxiliary results in combination
with a procedure or technique. Table 4 gives the distribu-
Explain precisely what you have contributed to the
tion of submissions to ICSE 2002, based on reading the
store of software engineering knowledge and how this is
abstracts (but not the papers), followed by graphs of the
useful beyond your own project.
counts and distributions in Figures 3 and 4.
Table 3. Types of software engineering research results
Type of result
Examples
Procedure or
New or better way to do some task, such as design, implementation, maintenance,
technique
measurement, evaluation, selection from alternatives; includes techniques for
implementation, representation, management, and analysis; a technique should be
operational—not advice or guidelines, but a procedure
Qualitative or
Structure or taxonomy for a problem area; architectural style, framework, or design pattern;
descriptive model
non-formal domain analysis, well-grounded checklists, well-argued informal
generalizations, guidance for integrating other results, well-organized interesting
observations
Empirical model
Empirical predictive model based on observed data
Analytic model
Structural model that permits formal analysis or automatic manipulation
Tool or notation
Implemented tool that embodies a technique; formal language to support a technique or model
(should have a calculus, semantics, or other basis for computing or doing inference)
Specific solution,
Solution to application problem that shows application of SE principles – may be design,
prototype, answer,
prototype, or full implementation; careful analysis of a system or its development, result of
or judgment
a specific analysis, evaluation, or comparison
Report
Interesting observations, rules of thumb, but not sufficiently general or systematic to rise to the
level of a descriptive model.
Table 4. Types of research results represented in ICSE 2002 submissions and acceptances
Type of result
Submitted
Accepted
Ratio Acc/Sub
Procedure or technique
152(44%)
28 (51%)
18%
Qualitative or descriptive model
50 (14%)
4 (7%)
8%
Empirical
model
4 (1%)
1 (2%)
25%
Analytic
model
48
(14%)
7 (13%)
15%
Tool or notation
49 (14%)
10 (18%)
20%
Specific solution, prototype, answer, or judgment
34 (10%)
5 (9%)
15%
Report
11
(3%)
0 (0%)
0%
TOTAL
348(100.0%)
55 (100.0%)
16%
Result
Result
350
100%
300
80%
250
200
60%
150
40%
100
50
20%
0
0%
d
d
l
ch
od
od
m
sol
tal
ch
od
od
tal
Te
mo
p
mo
Tool
m
Too
Report To
Te
p m
ec sol Report To
Qual
Em
Anal
Spec
Qual m Em
Anal
Sp
Accepted
Rejected
Accepted
Rejected
Figure 3. Counts of acceptances and rejections
Figure 4. Distribution of acceptances and rejections
by type of result
by type of result
The number of results is larger than the number of
use in other settings. If that idea is increased
papers because 50 papers included a supporting result,
confidence in the tool or technique, show how your
usually a tool or a qualitative model.
experience should increase the reader's confidence
Research projects commonly produce results of several
for applications beyond the example of the paper.
kinds. However, conferences, including ICSE, usually
What’s new here?
impose strict page limits. In most cases, this provides too
little space to allow full development of more than one
The program committee wants to know what is novel
idea, perhaps with one or two supporting ideas. Many
or exciting, and why. What, specifically, is the
authors present the individual ideas in conference papers,
contribution? What is the increment over earlier work by
and then synthesize them in a journal article that allows
the same authors? by other authors? Is this a sufficient
space to develop more complex relations among results.
increment, given the usual standards of subdiscipline?
Above all, the program committee also wants to know
3.3 What do program committees look for?
what you actually contributed to our store of knowledge
The program committee looks for interesting, novel,
about software engineering. Sure, you wrote this tool and
exciting results that significantly enhance our ability to
tried it out. But was your contribution the technique that is
develop and maintain software, to know the quality of the
embedded in the tool, or was it making a tool that’s more
software we develop, to recognize general principles
effective than other tools that implement the technique, or
about software, or to analyze properties of software.
was it showing that the tool you described in a previous
You should explain your result in such a way that
paper actually worked on a practical large-scale problem?
someone else could use your ideas. Be sure to explain
It’s better for you as the author to explain than for the
what’s novel or original – is it the idea, the application of
program committee to guess. Be clear about your claim …
the idea, the implementation, the analysis, or what?
Awful
▼ • I completely and generally solved …
Define critical terms precisely. Use them consistently.
(unless you actually did!)
The more formal or analytic the paper, the more important
Bad
▼ • I worked on galumphing.
this is.
(or studied, investigated, sought,
Here are some questions that the program committee
explored)
may ask about your paper:
Poor
▼ • I worked on improving galumphing.
(or contributed to, participated in,
What, precisely, do you claim to contribute?
helped with)
Does your result fully satisfy your claims? Are the
Good
▲ • I showed the feasibility of composing
definitions precise, and are terms used consistently?
blitzing with flitzing.
Authors tend to have trouble in some specific
• I significantly improved the accuracy of
situations. Here are some examples, with advice for
the standard detector.
staying out of trouble:
(or proved, demonstrated, created,
♦ If your result ought to work on large systems, explain
established, found, developed)
why you believe it scales.
Better
▲ • I automated the production of flitz
♦ If you claim your method is "automatic", using it
tables from specifications.
should not require human intervention. If it's
• With a novel application of the blivet
automatic when it's operating but requires manual
transform, I achieved a 10% increase
assistance to configure, say so. If it's automatic
in speed and a 15% improvement in
except for certain cases, say so, and say how often
coverage over the standard method.
the exceptions occur.
Use verbs that show results and achievement, not just
♦ If you claim your result is "distributed", it probably
effort and activity.
should not have a single central controller or server.
If it does, explain what part of it is distributed and
"Try not. Do, or do not. There is no try." -- Yoda .
what part is not.
What has been done before? How is your work different
♦ If you're proposing a new notation for an old
or better?
problem, explain why your notation is clearly
superior to the old one.
What existing technology does your research build on?
♦ If your paper is an "experience report", relating the
What existing technology or prior research does your
use of a previously-reported tool or technique in a
research provide a superior alternative to? What’s new
practical software project, be sure that you explain
here compared to your own previous work? What
what idea the reader can take away from the paper to
alternatives have other researchers pursued, and how is
your work different or better?
As in other areas of science and engineering, software
If your contribution is principally the synthesis or
engineering knowledge grows incrementally. Program
integration of other results or components, be clear about
committees are very interested in your interpretation of
why the synthesis is itself a contribution. What is novel,
prior work in the area. They want to know how your work
exciting, or nonobvious about the integration? Did you
is related to the prior work, either by building on it or by
generalize prior results? Did you find a better
providing an alternative. If you don’t explain this, it’s
representation? Did your research improve the individual
hard for the program committee to understand how you’ve
results or components as well as integrating them? A
added to our store of knowledge. You may also damage
paper that simply reports on using numerous elements
your credibility if the program committee can’t tell
together is not enough, even if it's well-engineered. There
whether you know about related work.
must be an idea or lesson or model that the reader can take
Explain the relation to other work clearly …
from the paper and apply to some other situation.
Awful ▼ The galumphing problem has attracted
If your paper is chiefly a report on experience
much attention [3,8,10,18,26,32,37]
applying research results to a practical problem, say what
Bad
▼ Smith [36] and Jones [27] worked on
the reader can learn from the experience. Are your
galumphing.
conclusions strong and well-supported? Do you show
Poor
▼ Smith [36] addressed galumphing by
comparative data and/or statistics? An anecdotal report on
blitzing, whereas Jones [27] took a
a single project is usually not enough. Also, if your report
flitzing approach.
mixes additional innovation with validation through
experience, avoid confusing your discussion of the
Good
▲ Smith’s blitzing approach to galumphing
innovation with your report on experience. After all, if
[36] achieved 60% coverage [39].
you changed the result before you applied it, you're
Jones [27] achieved 80% by flitzing,
evaluating the changed result. And if you changed the
but only for pointer-free cases [16].
result while you were applying it, you may have
Better ▲ Smith’s blitzing approach to galumphing
confounded the experiences with the two versions.
[36] achieved 60% coverage [39].
Jones [27] achieved 80% by flitzing,
If a tool plays a featured role in your paper, what is
but only for pointer-free cases [16].
the role of the tool? Does it simply support the main
We modified the blitzing approach to
contribution, or is the tool itself a principal contribution,
use the kernel representation of flitzing
or is some aspect of the tool’s use or implementation the
and achieved 90% coverage while
main point? Can a reader apply the idea without the tool?
relaxing the restriction so that only
If the tool is a central part of result, what is the technical
cyclic data structures are prohibited.
innovation embedded in the tool or its implementation?
What, precisely, is the result?
If a system implementation plays a featured role in
your paper, what is the role of the implementation? Is the
Explain what your result is and how it works. Be
system sound? Does it do what you claim it does? What
concrete and specific. Use examples.
ideas does the system demonstrate?
If you introduce a new model, be clear about its power.
♦ If the implementation illustrates an architecture or
How general is it? Is it based on empirical data, on a
design strategy, what does it reveal about the
formal semantics, on mathematical principles? How
architecture? What was the design rationale? What
formal is it—a qualitative model that provides design
were the design tradeoffs? What can the reader apply
guidance may be as valuable as a mathematical model of
to a different implementation?
some aspect of correctness, but they will have to satisfy
♦ If the implementation demonstrates an
different standards of proof. Will the model scale up to
implementation technique, how does it help the
problems of size appropriate to its domain?
reader use the technique in another setting?
If you introduce a new metric, define it precisely. Does
♦ If the implementation demonstrates a capability or
it measure what it purports to measure and do so better
performance improvement, what concrete evidence
than the alternatives? Why?
does it offer to support the claim?
♦ If the system is itself the result, in what way is it a
If you introduce a new architectural style, design
contribution to knowledge? Does it, for example,
pattern, or similar design element, treat it as if it were a
show you can do something that no one has done
new generalization or model. How does it differ from the
before (especially if people doubted that this could
alternatives? In what way is it better? What real problem
be done)?
does it solve? Does it scale?
4. Why should the reader believe your result?
research result and the method used to obtain the result.
As an obvious example, a formal model should be
Show evidence that your result is valid—that it actually
supported by rigorous derivation and proof, not by one or
helps to solve the problem you set out to solve.
two simple examples. On the other hand, a simple
4.1. What kinds of validation do software
example derived from a practical system may play a major
role in validating a new type of development method.
engineers do?
Table 5 lists the types of research validation that are used
Software engineers offer several kinds of evidence in
in software engineering research papers and provides
support of their research results. It is essential to select a
specific examples. In this table, the examples are keyed to
form of validation that is appropriate for the type of
the type of result they apply to.
Table 5. Types of software engineering research validation
Type of validation Examples
Analysis
I have analyzed my result and find it satisfactory through rigorous analysis, e.g. …
For a formal model
… rigorous derivation and proof
For an empirical model
… data on use in controlled situation
For a controlled experiment
… carefully designed experiment with statistically significant
results
Evaluation
Given the stated criteria, my result...
For a descriptive model
… adequately describes phenomena of interest …
For a qualitative model
… accounts for the phenomena of interest…
For an empirical model
… is able to predict … because …, or
… generates results that fit actual data …
Includes feasibility studies, pilot projects
Experience
My result has been used on real examples by someone other than me, and the evidence of its
correctness/usefulness/effectiveness is …
For a qualitative model
… narrative
For an empirical model or tool … data, usually statistical, on practice
For a notation or technique
… comparison of systems in actual use
Example
Here’s an example of how it works on
For a technique or procedure
…a "slice of life" example based on a real system …
For a technique or procedure
…a system that I have been developing …
For a technique or procedure … a toy example, perhaps motivated by reality
The "slice of life" example is most likely to be convincing, especially if accompanied by an
explanation of why the simplified example retains the essence of the problem being solved.
Toy or textbook examples often fail to provide persuasive validation, (except for standard
examples used as model problems by the field).
Persuasion
I thought hard about this, and I believe passionately that ...
For a technique
… if you do it the following way, then …
For a system
… a system constructed like this would …
For a model
… this example shows how my idea works
Validation purely by persuasion is rarely sufficient for a research paper. Note, though, that if the
original question was about feasibility, a working system, even without analysis, can suffice
Blatant assertion
No serious attempt to evaluate result. This is highly unlikely to be acceptable
4.2 Which of these are most common?
The most successful kinds of validation were based on
analysis and real-world experience. Well-chosen examples
Alas, well over a quarter of the ICSE 2002 abstracts
were also successful. Persuasion was not persuasive, and
give no indication of how the paper's results are validated,
narrative evaluation was only slightly more successful.
if at all. Even when the abstract mentions that the result
Table 6 gives the distribution of submissions to ICSE
was applied to an example, it was not always clear
2002, based on reading the abstracts (but not the papers),
whether the example was a textbook example, or a report
followed by graphs of the counts and distributions.
on use in the field, or something in between.
Figures 5 and 6 show these counts and distributions.
Table 6. Types of research validation represented in ICSE 2002 submissions and acceptances
Type of validation
Submitted
Accepted
Ratio Acc/Sub
Analysis
48
(16%)
11
(26%)
23%
Evaluation
21
(7%)
1 (2%)
5%
Experience
34
(11%)
8 (19%)
24%
Example
82
(27%)
16
(37%)
20%
Some example, can't tell whether it's toy or actual use
6 (2%)
1 (2%)
17%
Persuasion
25
(8%)
0 (0.0%)
0%
No mention of validation in abstract
84 (28%)
6 (14%)
7%
TOTAL
300(100.0%)
43 (100.0%)
14%
Validation
Validation
300
100%
250
80%
200
60%
150
40%
100
50
20%
0
0%
e
?
e
e
?
e
sis
ple
ion
al
ple
ion
al
uade
ysis
uade
, kind aluat
ent
Tot
ent
Tot
Analy
Exam
Ev
m
Anal
Exam
Evaluat
m
Experienc
Ex
Pers No
Experienc
Ex, kind
Pers No
Accepted
Rejected
Accepted
Rejected
Figure 5. Counts of acceptances and rejections
Figure 6. Distribution of acceptances and rejections
by type of validation
by type of validation
4.3 What do program committees look for?
Is the validation related to the claim? If you're claiming
performance improvement, validation should analyze
The program committee looks for solid evidence to
performance, not ease of use or generality. And
support your result. It's not enough that your idea works
conversely.
for you, there must also be evidence that the idea or the
Is this such an interesting, potentially powerful idea
technique will help someone else as well.
that it should get exposure despite a shortage of concrete
The statistics above show that analysis, actual
evidence?
experience in the field, and good use of realistic examples
Authors tend to have trouble in some specific
tend to be the most effective ways of showing why your
situations. Here are some examples, with advice for
result should be believed. Careful narrative, qualitative
staying out of trouble:
analysis can also work if the reasoning is sound.
♦ If you claim to improve on prior art, compare your
Why should the reader believe your result?
result objectively to the prior art.
Is the paper argued persuasively? What evidence is
♦ If you used an analysis technique, follow the rules of
presented to support the claim? What kind of evidence is
that analysis technique. If the technique is not a
offered? Does it meet the usual standard of the
common one in software engineering (e.g., meta-
subdiscipline?
analysis, decision theory, user studies or other
Is the kind of evaluation you're doing described clearly
behavioral analyses), explain the technique and
and accurately? "Controlled experiment" requires more
standards of proof, and be clear about your
than data collection, and "case study" requires more than
adherence to the technique.
anecdotal discussion. Pilot studies that lay the groundwork
♦ If you offer practical experience as evidence for your
for controlled experiments are often not publishable by
result, establish the effect your research has. If at all
themselves.
possible, compare similar situations with and without
your result.
♦ If you performed a controlled experiment, explain the
When I advise PhD students on the validation section
experimental design. What is the hypothesis? What is
of their theses, I offer the following heuristic: Look
the treatment? What is being controlled? What data
carefully at the short statement of the result—the principal
did you collect, and how did you analyze it? Are the
claim of the thesis. This often has two or three clauses
results significant? What are the potentially
(e.g., I found an efficient and complete method …"); if so,
confounding factors, and how are they handled? Do
each presents a separate validation problem. Ask of each
the conclusions follow rigorously from the
clause whether it is a global statement ("always", "fully"),
experimental data?
a qualified statement ("a 25% improvement", "for
♦ If you performed an empirical study, explain what
noncyclic structures…"), or an existential statement {"we
you measured, how you analyzed it, and what you
found an instance of"). Global statements often require
concluded. What data did you collect, and how? How
analytic validation, qualified statements can often be
is the analysis related to the goal of supporting your
validated by evaluation or careful examination of
claim about the result? Do not confuse correlation
experience, and existential statements can sometimes be
with causality.
validated by a single positive example. A frequent result
♦ If you use a small example for explaining the result,
of this discussion is that students restate the thesis claims
provide additional evidence of its practical use and
to reflect more precisely what the theses actually achieve.
scalability.
If we have this discussion early enough in the thesis
process, students think about planning the research with
5. How do you combine the elements into a
demonstrable claims in mind.
research strategy?
Concretely, Table 7 shows the combinations that were
represented among the accepted papers at ICSE 2002,
It is clear that not all combinations of a research
omitting the 7 for which the abstracts were unclear about
question, a result, and a validation strategy lead to good
validation:
research. Software engineering has not developed good
Table 7. Paradigms of ICSE2002 acceptances
general guidance on this question.
Question Result
Validation
#
Tables 1, 3, and 5 define a 3-dimensional space. Some
Devel method
Procedure
Analysis
2
portions of that space are densely populated: One
common paradigm is to find a better way to perform some
Devel method
Procedure
Experience
3
software development or maintenance task, realize this in
Devel method
Procedure
Example
3
a concrete procedure supported by a tool, and evaluate the
Devel method
Qual model
Experience
2
effectiveness of this procedure and tool by determining
Devel method
Analytic model
Experience
2
how its use affects some measure (e.g., error rates) of
Devel method
Notation or tool
Experience
1
quality. Another common paradigm is to find a better way
Analysis method
Procedure
Analysis
5
to evaluate a formalizable property of a software system,
Analysis method
Procedure
Evaluation
1
develop a formal model that supports inference, and to
Analysis method
Procedure
Experience
2
show that the new model allows formal analysis or proof
Analysis method
Procedure
Example
6
of the properties of interest.
Analysis method
Analytic model
Experience
1
Clearly, the researcher does not have free choice to
Analysis method
Analytic model
Example
2
mix and match the techniques—validating the correctness
of a formal model through field study is as inappropriate
Analysis method
Tool
Analysis
1
as attempting formal verification of a method based on
Eval of instance
Specific analysis
Analysis
3
good organization of rules of thumb.
Eval of instance
Specific analysis
Example
2
Selecting a type of result that will answer a given
question usually does not seem to present much difficulty,
6. Does the abstract matter?
at least for researchers who think carefully about the
The abstracts of papers submitted to ICSE convey a
choice. Blindly adopting the research paradigm someone
sense of the kinds of research submitted to the conference.
used last year for a completely different problem is a
Some abstracts were easier to read and (apparently) more
different case, of course, and it can lead to serious misfits.
informative than others. Many of the clearest abstracts had
Choosing a good form of validation is much harder,
a common structure:
and this is often a source of difficulty in completing a
♦ Two or three sentences about the current state of the
successful paper. Table 6 shows some common good
art, identifying a particular problem
matches. This does not, unfortunately, provide complete
♦ One or two sentences about what this paper
guidance.
contributes to improving the situation
♦ One or two sentences about the specific result of the
writing a good systems paper [11]. USENIX now provides
paper and the main idea behind it
this advice to its authors. Also in the systems vein,
♦ A sentence about how the result is demonstrated or
Partridge offers advice on "How to Increase the Chances
defended
Your Paper is Accepted at ACM SIGCOMM" [15].
Abstracts in roughly this format often explained clearly
SIGCHI offers a "Guide to Successful Papers
what readers could expect in the paper.
Submission" that includes criteria for evaluation and
Acceptance rates were highest for papers whose
discussion of common types of CHI results, together with
abstracts indicate that analysis or experience provides
how different evaluation criteria apply for different types
evidence in support of the work. Decisions on papers were
of results [13]. A study [8] of regional factors that affect
made on the basis of the whole papers, of course, not just
acceptance found regional differences in problems with
the abstracts—but it is reasonable to assume that the
novelty, significance, focus, and writing quality.
abstracts reflect what's in the papers.
In 1993, the SIGGRAPH conference program chair
Whether you like it or not, people judge papers by their
wrote a discussion of the selection process, "How to Get
abstracts and read the abstract in order to decide whether
Your SIGGRAPH Paper Rejected" [10]. The 2003
to read the whole paper. It's important for the abstract to
SIGGRAPH call for papers [21] has a description of the
tell the story. Don't assume, though, that simply adding a
review process and a frequently-asked questions section
sentence about analysis or experience to your abstract is
with an extensive set of questions on "Getting a Paper
sufficient; the paper must deliver what the abstract
Accepted".
promises
7.3. What about this report itself?
7. Questions you might ask about this report
People have asked me, "what would happen if you
submitted this to ICSE?" Without venturing to predict
7.1. Is this a sure-fire recipe?
what any given ICSE program committee would do, I note
that as a research result or technical paper (a "finding" in
No, not at all. First, it's not a recipe. Second, not all
Brooks' sense [3]) it falls short in a number of ways:
software engineers share the same views of interesting and
♦ There is no attempt to show that anyone else can
significant research. Even if your paper is clear about
apply the model. That is, there is no demonstration of
what you’ve done and what you can conclude, members of
inter-rater reliability, or for that matter even
a program committee may not agree about how to
repeatability by the same rater.
interpret your result. These are usually honest technical
♦ The model is not justified by any principled analysis,
disagreements, and committee members will try hard to
though fragments, such as the types of models that
understand what you have done. You can help by
can serve as results, are principled. In defense of the
explaining your work clearly; this report should help you
model, Bowker and Starr [2] show that useful
do that.
classifications blend principle and pragmatic
7.2 Is ICSE different from other conferences?
descriptive power.
♦ Only one conference and one program committee is
ICSE recognizes several distinct types of technical
reflected here.
papers [6]. For 2002, they were published separately in
♦ The use of abstracts as proxies for full papers is
the proceedings
suspect.
Several other conferences offer "how to write a paper"
♦ There is little discussion of related work other than
advice:
the essays about writing papers for other
In 1993, several OOPSLA program committee veterans
conferences. Although discussion of related work
gave a panel on "How to Get a Paper Accepted at
does appear in two complementary papers [19, 20],
OOPSLA" [9]. This updated the 1991 advice for the same
this report does not stand alone.
conference [14]
On the other hand, I believe that this report does meet
SIGSOFT offers two essays on getting papers
Brooks' standard for "rules of thumb" (generalizations,
accepted, though neither was actually written for a
signed by the author but perhaps incompletely supported
software engineering audience. They are "How to Have
by data, judged by usefulness and freshness), and I offer it
Your Abstract Rejected" [26] (which focuses on
in that sense.
theoretical papers) and "Advice to Authors of Extended
Abstracts", which was written for PLDI. [16].
8. Acknowledgements
Rather older, Levin and Reddell, the 1983 SOSP
(operating systems) program co-chairs offered advice on
This work depended critically on access to the entire
body of submitted papers for the ICSE 2002 conference,
which would not have been possible without the
ACM SIGCHI Human Factors in Computer Systems Conf
cooperation and encouragement of the ICSE 2002
(CHI '94), pp.278-284.
program committee. The development of these ideas has
13. William Newman et al. Guide to Successful Papers
also benefited from discussion with the ICSE 2002
Submission at CHI 2001. http://acm.org/sigs/sigchi/
program committee, with colleagues at Carnegie Mellon,
chi2001/call/submissions/guide-papers.html
and at open discussion sessions at FSE Conferences. The
14. OOPSLA '91 Program Committee. How to get your paper
work has been supported by the A. J. Perlis Chair at
accepted at OOPSLA. Proc OOPSLA'91, pp.359-363.
Carnegie Mellon University.
http://acm.org/sigplan/oopsla/oopsla96/how91.html
15. Craig Partridge. How to Increase the Chances your Paper
9. References
is Accepted at ACM SIGCOMM.
http://www.acm.org/sigcomm/conference-misc/author-guide.html
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